Uniform Deployment Research Articles

UAV Jamming Localization Against Random Eavesdropper Positions Using Cuckoo Search Algorithm

ABSTRACTUnmanned Aerial Vehicle (UAV) jammer localization refers to the process of determining the optimal location for a UAV jammer to be placed to interfere with an eavesdropper's communication with a target. In fact, the main goal is to maximize the jamming signal power at the eavesdropper's position while minimizing the jamming signal power at the target's position. This paper leverages Cuckoo Search algorithm for the secrecy rate optimization which refers to the amount of secure information transmitted from the target to the intended receiver. The model is composed with an UAV jammer, two ground nodes and an illegitimate user with unknown position. True uniform random deployment processes is used to model the randomness of the eavesdropper location. Further, a novel summed secrecy approach is proposed to determine the overall optimal UAV position. Simulations demonstrate the superiority of the novel approach versus other benchmark algorithms across various numbers of eavesdroppers.

Delay-Oriented Roadside Unit Deployment for Highway Intersections in Vehicular Ad Hoc Networks.

Optimizing the deployment of roadside units (RSUs) holds great potential for enhancing the delay performance of vehicular ad hoc networks. However, there has been limited focus on devising RSU deployment strategies tailored specifically for highway intersections. In this study, we introduce a novel probabilistic model to characterize events occurring around highway intersections. By leveraging this model, we analytically determine the expected event reporting delays for both highway segments and intersections. Subsequently, we propose an RSU deployment scheme specifically designed for highway intersections, aimed at minimizing the expected event reporting delay. To implement this scheme, we introduce an innovative algorithm named cooperative walking. Through illustrative examples, we demonstrate that our proposed RSU deployment strategy for highway intersections outperforms the commonly employed uniform RSU deployment scheme and the previously proposed balloon method in terms of delay performance.

Flow Diverter Performance Comparison of Different Wire Materials for Effective Intracranial Aneurysm Treatment.

A flow diverter (FD) is an effective method for treating wide-necked intracranial aneurysms by inducing hemodynamic changes in aneurysms. However, the procedural technique remains challenging, and it is often not performed properly in many cases of deployment or placements. In this study, three types of FDs that changed the material of the wire were prepared within the same structure. Differences in physical properties, such as before and after delivery loading stent size, radial force, and radiopacity, were evaluated. The performances in terms of deployment and trackability force were also evaluated in a simulated model using these FDs. Furthermore, changes of deployment patterns when these FDs were applied to a 3D-printed aneurysm model were determined. The NiTi FD using only nitinol (NiTi) wire showed 100% size recovery and 42% to 45% metal coverage after loading. The low trackability force (10.9 to 22.9 gf) allows smooth movement within the delivery system. However, NiTi FD cannot be used in actual surgeries due to difficulties in X-ray identification. NiTi-Pt/W FD, a combination of NiTi wire and platinum/tungsten (Pt/W) wire, had the highest radiopacity and compression force (6.03 ± 0.29 gf) among the three FDs. However, it suffered from high trackability force (22.4 to 39.9 gf) and the end part braiding mesh tended to loosen easily, so the procedure became more challenging. The NiTi(Pt) FD using a platinum core nitinol (NiTi(Pt)) wire had similar trackability force (11.3 to 22.1 gf) to NiTi FD and uniform deployment, enhancing procedural convenience. However, concerns about low expansion force (1.79 ± 0.30 gf) and the potential for migration remained. This comparative analysis contributes to a comprehensive understanding of how different wire materials influence the performance of FDs. While this study is still in its early stages and requires further research, its development has the potential to guide clinicians and researchers in optimizing the selection and development of FDs for the effective treatment of intracranial aneurysms.

Heterogeneous effects of battery storage deployment strategies on decarbonization of provincial power systems in China

Battery storage is critical for integrating variable renewable generation, yet how the location, scale, and timing of storage deployment affect system costs and carbon dioxide (CO2) emissions is uncertain. We improve a power system model, SWITCH-China, to examine three nationally uniform battery deployment strategies (Renewable-connected, Grid-connected, and Demand-side) and a heterogeneous battery deployment strategy where each province is allowed to utilize any of the three battery strategies. Here, we find that the heterogeneous strategy always provides the lowest system costs among all four strategies, where provinces with abundant renewable resources dominantly adopt Renewable-connected batteries while those with limited renewables dominantly adopt Demand-side batteries. However, which strategy achieves the lowest CO2 emissions depends on carbon prices. The Renewable-connected strategy achieves the lowest CO2 emissions when carbon prices are relatively low, and the heterogeneous strategy results in the lowest CO2 emissions only at extremely high carbon prices.

Infrastructure Allocation for Improving Sensing Accuracy and Connectivity Probability Based on Combination Strategy in Vehicular Networks

Sensing and communication are the two major concerns in vehicle-road collaboration system, whose function realization needs the support of RSE (roadside equipment). A misleading deployment incurs in waste of valuable resources and degradation of the network performance. Based on the combination strategy, this study proposed a hybrid allocation method to deploy RSE on a highway by considering the sensing capability of traditional ITS sensors and the communication feature of popular networking devices. Two sorts of RSE, the one integrated ITS sensor and networking device, named sensing & communication-RSE (scRSE), and the other belongs to networking devices, named communication-RSE (cRSE), were involved. A hybrid RSE allocation framework was proposed, in which an optimal method was adopted to deploy scRSE on key points for data acquisition and a connectivity probability-based uniform method was used to deploy cRSE for communication. The effectiveness of these methods was verified by case analysis. The results indicated that the proposed hybrid method was very effective and outperformed the conventional uniform deployment method. The key parameters, including the coverage range, RSE spacing, and vehicle density, had an important impact on network connectivity probability. To obtain high connectivity probability, a lower density of connected vehicles (CVs) needs the support of wide coverage of RSE, and vice versa. When the density of CVs reaches to a certain value, vehicular networks could be realized without the help of RSE. Benefitting from the mobility of CVs, increasing their transmission range was better than increasing RSE coverage radius in improving connectivity probability.

Almost uniform deployment of mobile agents in dynamic rings

In this paper, we consider the almost uniform deployment problem of mobile agents in dynamic rings, which requires all agents other than one agent to spread uniformly in the ring. In this paper, we consider this problem in 1-interval connected rings, that is, one of the links may be missing at each time step. Focusing on global knowledge given to agents, we clarify the problem solvability and the algorithm performance. First, we consider agents with knowledge of the number n of nodes. Then, we show that the problem can be solved with O(klog⁡n) memory space per agent, O(nlog⁡k) rounds, and a total number of O(kn) moves, where k is the number of agents. Next, we consider agents with knowledge of k. Then, we show that the problem can be solved with O(klog⁡n) memory space per agent, O(n2) rounds, and a total number of O(n2) moves.

Optimal deployment of swarm positions in cooperative interception of multiple UAV swarms

In order to prevent the attacker from breaking through the blockade of the interception, deploying multiple Unmanned Aerial Vehicle (UAV) swarms on the interception line is a new combat style. To solve the optimal deployment of swarm positions in the cooperative interception, an optimal deployment optimization model is presented by minimizing the penetration zones' area and the analytical expression of the optimal deployment positions is deduced. Firstly, from the view of the attackers breaking through the interception line, the situations of vertical penetration and oblique penetration are analyzed respectively, and the mathematical models of penetration zones are obtained under the condition of a single UAV swarm and multiple UAV swarms. Secondly, based on the optimization goal of minimizing the penetration area, the optimal deployment optimization model for swarm positions is proposed, and the analytical solution of the optimal deployment is solved by using the convex programming theory. Finally, the proposed optimal deployment is compared with the uniform deployment and random deployment to verify the validity of the theoretical analysis.

Delay-Aware Admission Control and Beam Allocation for 5G Functional Split Enhanced Millimeter Wave Wireless Fronthaul Networks

In this paper, we study a delay-aware admission control (AC) and beam allocation (BA) problem with the consideration of quality of experience (QoE) and dynamic variation of channel condition for centralized unit (CU) and distributed unit (DU) based functional split options (FSOs) in millimeter wave (mmWave) fronthaul downlink networks. The original optimization problem aims to maximize time-average QoE subject to delay and queue stability constraints. The intractability of the considered problem comes from the necessity to allocate resources across periods of time slots. After applying virtual queue transformation and Lyapunov optimization method, the targeting problem can be converted into two independent AC and BA sub-problems in each time slot. The converted problem involves actual and virtual queues, which causes conflicting tendency. With the design of a flexible Lyapunov function, the influences of actual and virtual queues are theoretically to be proved balanced. Moreover, the AC sub-problem can be solved by Karush-Kuhn-Tucker condition, whereas mmWave BA sub-problem is tackled by genetic algorithm and matching game for the CU-based and DU-based FSOs, respectively. Simulation results demonstrate the applicability of the proposed CU-/DU-based algorithms in terms of average data rate, admission control rate, queue length, and corresponding system delay under either uniform or dense deployment of DUs. Moreover, the proposed scheme achieves the lowest delay and highest QoE performances while sustaining the system stability compared with the state-of-the-art mechanisms in open literatures.

Environment adaptive deployment of water quality sensor networks

Water quality sensor networks can be used for monitoring water environment, early warning and prevention of water pollution through accurate collection of water quality information. Effective deployment of the network can improve its monitoring efficiency. After the uniform deployment of the network, the sensor nodes need to be deployed in the key areas reasonably, so as to save the hardware cost and improve the monitoring effect. In this paper, the water area characteristic model is established to get the key monitoring area. Besides, the Gaussian plume model is applied to obtain the impact range of the key monitoring areas. The experimental results show that Qianhai is the key monitoring area, and its impact range is 10.36 m. On this basis, we deploy the sensors using particle swarm optimisation. Simulation results show that key area can be monitored better, whereas other regions can still guarantee a maximum coverage with a total coverage rate of 79.09%.

Retracted] Internet of Things Security Detection Technology Based on Grey Association Decision Algorithm

This paper analyzes the current real‐time monitoring system based on grey‐related IoT security sensors for the detection of risk factors in the production environment of the Internet of Things and proposes a design plan for the Internet of Things environment monitoring based on the grey‐related Internet of Things security sensor network, and according to the reliability guarantee mechanism of the system, a three‐dimensional uniform IoT node deployment method suitable for IoT security monitoring is proposed. Based on the grey correlation analysis, it can provide a quantitative measurement analysis for the development and change state of a system, which is very suitable for the analysis of dynamic operating systems. As a real‐time dynamic system of the Internet of Things, the use of grey correlation method to analyze its network security status has good operability and practical value. According to the multisource information processing technology, the monitoring data are preprocessed by dynamic limiting filtering, and then the data are fused at the data level with the optimal weighting algorithm. Through the use of grey correlation analysis to quantify the relative impact of cyberattacks on the network within a certain period of time, the quantitative assessment of the security environment and status of the entire network is realized. Finally, the characteristics of grey relational analysis and rough set theory attribute reduction are used to form the basis of grey correlation decision‐level fusion algorithm, to achieve effective processing of the data of the Internet of Things security monitoring system.

Sensor placement optimization for critical-grid coverage problem of indoor positioning

It is more practical and efficient to deploy sensors in critical areas rather than common areas to ensure indoor positioning accuracy and reduce deployment cost. This study focused on the sensor placement optimization for critical-grid coverage problem with two objectives: accuracy and cost. After reviewing some related works, this article proposed a multi-objective optimization model for critical-grid coverage problem of indoor positioning considering k-coverage problem as well as the topological rationality of sensor distribution. Then, NSGA-II algorithm was used to solve the optimizing model of sensor placement. At last, the simulation experiment and real environment validation were conducted for proposed method. The results showed that the optimized schemes obtain a lower error (1.13, 1.21 m) and a higher reduction of sensor deployment cost than the uniform deployment scheme (1.44 m). As a conclusion, the proposed method could reduce the cost of sensor deployment while ensuring the accuracy of indoor positioning for critical areas. It also provides a new direction for improving the accuracy of indoor positioning.

Optimal Relay Placement in Magnetic Induction-Based Internet of Underwater Things

The advances in developing the Internet of Underwater Things (IoUT) can lead to numerous applications, namely, environmental monitoring, underwater navigation, and surveillance. Magnetic Induction (MI) is a promising communication solution for IoUT networks. In this article, we focus on a deep-ocean monitoring network, where the underwater relays communicate using MI induction. Placement of the relays in such a system is an exciting and challenging task. Therefore, we propose an optimal relay placement solution for relays in MI-based IoUT networks to improve network throughput. First, we formulate the relay placement problem as a non-convex optimization problem. Then, we propose a global optimization technique based on Reverse Convex Programming (RCP) to solve the non-convex function. Finally, we compare the results of the proposed scheme with the uniform deployment to show its effectiveness.

Optimal Non-Uniform Deployments of LoRa Networks

LoRa wireless technology is an increasingly prominent solution for massive connectivity and the Internet of Things. Stochastic geometry and numerical analysis of LoRa networks usually consider uniform end-device deployments. Real deployments however will often be non-uniform, for example due to mobility. This letter mathematically investigates how non-uniform deployments affect network coverage and suggest optimal deployment strategies and uplink random access transmission schemes. We find that concave deployments of LoRa end-devices with a sub-linear spread of random access inter-transmission times provide optimal network coverage performance.

On the Lifetime of Asynchronous Software-Defined Wireless Sensor Networks

In this article, we consider a software-defined wireless sensor network (WSN) architecture which conserves energy by applying asynchronous duty cycling. In asynchronous sensor networks, the overhearing adversely impacts the energy consumption of the nodes. Using a mathematical model, we compute the maximum lifetime of the network and accordingly propose a multichannel operation and transmit power control to reduce the effect of overhearing in asynchronous networks. Our comprehensive test results confirm that for the network with load-aware nonuniform sensor node deployment, the network lifetime is much higher compared to a uniform deployment. We also show that the use of data aggregation software-defined WSNs (SDWSNs) can improve the network lifetime as compared to the data gathering networks.

Performance Analysis of Cell-Free Massive MIMO Systems: A Stochastic Geometry Approach

Cell-free (CF) massive multiple-input-multiple-output (MIMO) has emerged as an alternative deployment for conventional cellular massive MIMO networks. As revealed by its name, this topology considers no cells, while a large number of multi-antenna access points (APs) serves simultaneously a smaller number of users over the same time/frequency resources through time-division duplex (TDD) operation. Prior works relied on the strong assumption (quite idealized) that the APs are uniformly distributed, and actually, this randomness was considered during the simulation and not in the analysis. However, in practice, ongoing and future networks become denser and increasingly irregular. Having this in mind, we consider that the AP locations are modeled by means of a Poisson point process (PPP) which is a more realistic model for the spatial randomness than a grid or uniform deployment. In particular, by virtue of stochastic geometry tools, we derive both the downlink coverage probability and achievable rate. Notably, this is the only work providing the coverage probability and shedding light on this aspect of CF massive MIMO systems. Focusing on the extraction of interesting insights, we consider small-cells (SCs) as a benchmark for comparison. Among the findings, CF massive MIMO systems achieve both higher coverage and rate with comparison to SCs due to the properties of favorable propagation, channel hardening, and interference suppression. Especially, we showed for both architectures that increasing the AP density results in a higher coverage which saturates after a certain value and increasing the number of users decreases the achievable rate but CF massive MIMO systems take advantage of the aforementioned properties, and thus, outperform SCs. In general, the performance gap between CF massive MIMO systems and SCs is enhanced by increasing the AP density. Another interesting observation concerns that a higher path-loss exponent decreases the rate while the users closer to the APs affect more the performance in terms of the rate.

Clustering Algorithm for Deployment of Independent Heterogeneous Wireless Sensor Network

Wireless Sensor Network (WSN) is the main element of modern Industrial Internet of Things system in event monitoring. However, WSN is unable to respond for longer life due to the limited battery backup. So, it is hard to monitor the event of interest for longer span of time with deployed WSN. This can be resolved by utilizing energy efficient design approach for data communication. Cluster based routing scheme could be the most prominent design approach for the said option. Nevertheless, on the cost of certain design constraints. Although, most of the real-life applications are designed based on clustering scheme. But, it utilizes random node deployment strategy to support assumptions of prethrown nodes in test field. Similarly, uniform node deployment is also being demanded in some specific applications. Thus, there is a high need to design and implement clustering protocol that can work satisfactorily independent of the node deployment. This paper presents an innovative design approach with Clustering Algorithm for Deployment of Independent Heterogeneous Wireless Sensor Network. In proposed approach cluster head (CH) is selected based on index modeling with available node attributes. Proposed design ensures selected CH offers better node degree and intra-connectivity inside the network. Here, we try to minimize the efforts of CH and cluster member such that energy efficiency of each individual node get extend to a high level. Hence, it improves stability period, lifetime and throughput of the network. Simulation of proposed approach outperforms the existing LEACH, SEP and DEEC by more than 1.3 times.

Adaptive repair algorithm for TORA routing protocol based on flood control strategy

Temporally Ordered Routing Algorithms (TORA) is one of the most representative on-demand routing protocols in Mobile Ad Hoc Networks (MANET). This protocol is widely used in high-speed mobile MANET because of its robust topology. In order to improve the shortcomings of TORA in routing maintenance, such as high overhead and delay, and make the routing more suitable for emergency and disaster relief network, an adaptive repair algorithm for TORA routing protocol based on flood control strategy (AR-TORA-FCS) is proposed. Firstly, according to the characteristics of the uniform deployment of the nodes in the network, the self-repair process of the self-repair nodes in the directed acyclic graph (DAG) of TORA is transformed into the optimal search problem for the optimal nodes, and the formula is established. According to Ray-Algorithm to search for the optimal node, it is proved that the search result is the optimal solution of the process of searching for the optimal node. Then, a conditional algorithm to determine the self-repair process is given, and the conditional threshold to initiate the self-repair process is determined. The mapping relationship between the repair process of self-repair nodes and the distance between the nodes is established, and the path repair is carried out before the path fails. In order to reduce control overhead, the definition of optimization region is given, and the algorithm to determine the optimization region is proposed. Simulation results show that the algorithm reduces control overhead, improves the packet delivery rate, and improves average end-to-end delay. The mobile device, which is subject to unified deployment, is used as a network node to test the proposed adaptive repair algorithm in the actual rescue and disaster relief environment. The results show that it is basically consistent with the simulation results, and the overall performance is improved significantly.

Space-efficient uniform deployment of mobile agents in asynchronous unidirectional rings

In this paper, we consider the uniform deployment problem of mobile agents in asynchronous unidirectional ring networks. This problem requires agents to spread uniformly in the network. In this paper, we focus on the memory space per agent required to solve the problem. We consider two problem settings. The first setting assumes that agents have no multiplicity detection, that is, agents cannot detect whether another agent is staying at the same node or not. In this case, we show that each agent requires Ω(log⁡n) memory space to solve the problem, where n is the number of nodes. In addition, we propose an algorithm to solve the problem with O(k+log⁡n) memory space per agent, where k is the number of agents. The second setting assumes that each agent is equipped with the weak multiplicity detection, that is, agents can detect whether another agent is staying at the same node or not, but cannot get any other information about the number of the agents. Then, we show that the memory space per agent can be reduced to O(log⁡k+log⁡log⁡n). To the best of our knowledge, this is the first research considering the effect of the multiplicity detection on memory space required to solve problems.

A comparative study of the effect of node distributions on 2D and 3D heterogeneous WSN

Two comparative studies of the impact of different node deployment strategies on 2D and 3D heterogeneous wireless sensor network (WSN) are conducted. Uniform, normal, and exponential node deployment distributions are utilised. Three 3D network geometries are introduced, namely, cube, sphere, and cylinder networks. Stable election protocol (SEP) is used to evaluate the performance of different node distributions in terms of network energy, throughput, stability period and network lifetime. Broadly speaking, it is noticed that normal distribution of nodes outperforms other distributions in the 2D and 3D comparative study, and in the study of different 3D network geometries with the exception of sphere network in which the uniform distribution performs almost the same as the normal distribution. It is also noticed that any node distribution performs better in a particular 3D network geometry than others. The paper also introduces the optimal cylinder network parameters for optimal network performance.

Proactive Uplink Interference Management for Nonuniform Heterogeneous Cellular Networks

In homogeneous cellular networks, fractional power control (FPC) is employed to partially compensate the path-loss and, hence, improve uplink ( $U_{L} $ ) signal-to-interference ratio (SIR). However, this scheme is less effective in heterogeneous cellular networks (HetNets) because: (i) except the typical user, all other users with variable $U_{L} $ transmit power (UTP) act as interferers, (ii) FPC leads to high UTP by edge users and, hence, more interference, and (iii) small base stations (SBSs)’ densification further increases network interferences. Leveraging FPC in HetNets, we propose nonuniform SBS deployment ( $\text {NU-SBS}_{\mathcal {D}} $ ) to reduce interference and, thus, increase network performance. According to our $\text {NU-SBS}_{\mathcal {D}} $ model, SBS deployment ( $\text {SBS}_{\mathcal {D}} $ ) near macro base station (MBS) is avoided, whereas MBS coverage edge area is enriched with ultra-dense $\text {SBS}_{\mathcal {D}} $ . $\text {NU-SBS}_{\mathcal {D}} $ model leads to: (i) better SIR reception of MBS coverage edge users, (ii) fewer $\text {SBS}_{\mathcal {D}} $ requirement, and (iii) better SBS coverage in the MBS coverage edge area. Moreover, to make a model more proactive, we also consider reverse frequency allocation (RFA) to further abate both $U_{L} $ and downlink $(D_{L}) $ interferences. The coverage probability expressions are derived for both uniform SBS deployment ( $\text {U-SBS}_{\mathcal {D}} $ ) and $\text {NU-SBS}_{\mathcal {D}} $ while using RFA and FPC. Through simulation and numerical results, we characterize coverage probability for different values of SIR threshold, path loss compensation factor, SBS density, users density, and the distance between the typical user and the associated base station. The proposed $\text {NU-SBS}_{\mathcal {D}} $ model along with RFA leads to reduced network interference as compared with $\text {U-SBS}_{\mathcal {D}} $ and, thus, leverages FPC in HetNets.