Random Deployment Research Articles

Cloud-Orchestrated Physical Topology Discovery of Large-Scale IoT Systems Using UAVs

Wireless sensor networks (WSNs) have been rapidly integrated into Internet of Things (IoT) systems, empowering rich and diverse applications such as large-scale environment monitoring. However, due to the random deployment of sensor nodes (SNs), physical topology of the WSNs cannot be controlled and typically remains unknown to the IoT cloud server. Therefore, in order to derive the physical topology at the cloud for effective real-time event detection, a cloud-orchestrated physical topology discovery scheme for large-scale IoT systems using unmanned aerial vehicles (UAVs) is proposed in this paper. More specifically, the large-scale monitoring area is first split into a number of subregions for UAV-enabled data collection. Within the subregions, parallel Metropolis-Hastings random walk (MHRW) is developed to gather the information of WSN nodes, including their IDs and neighbor tables. The collected information is then forwarded to the cloud through UAVs for the initial generation of logical topology. Thereafter, a network-wide 3-D localization algorithm is further developed based on the discovered logical topology and multidimensional scaling method (Topo-MDS), where the UAVs equipped with global positioning system are served as mobile anchors to locate the SNs. Simulation results indicate that the parallel MHRW improves both the efficiency and accuracy of logical topology discovery. In addition, the Topo-MDS algorithm dramatically improves the 3-D location accuracy, as compared to the existing algorithms in the literature.

BEHAVIORAL MODEL FOR DETECTION OF COMPROMISED NODES IN WSN

Node Compromise Detection (NCD) is an important requirement for dealing with potential attacks in regular and random deployment in Wireless Sensor Networks. Intrusion Detection method is proposed to detect the compromised nodes. We proposed Parameter Grouping (PG) model for compromised nodes. We reduced communication overhead , attestation overhead, false negatives and energy consumption when compared with existing methods, like ZoneTrust. An NS-2 based simulation work was carried out and found that the proposed PG model detects compromised nodes effectively.

Distributed coverage hole detection and recovery scheme for heterogeneous wireless sensor networks

In Wireless sensor networks (WSNs), the coverage problem is a fundamental issue. The coverage holes are generally caused by both, random deployment of the sensor nodes and node failures, and are hardly avoided in WSNs. It is impossible to recharge or replace the battery, and therefore, the collaborative detection and prediction of coverage holes and recovery of these holes has strategic importance in WSNs specially in heterogeneous WSNs (HWSNs). In this paper, a new scheme is proposed which includes distributed algorithms for detecting holes caused by both random deployment and node failures in HWSNs, where nodes can collaborate to detect and predict the coverage holes. This scheme also presents the new coverage holes healing algorithm, which can efficiently leverage mobility to optimize the average coverage rate and the average movement distance of the mobile nodes. The performance results of our scheme exceeds the existing algorithms in terms of coverage and energy saving.

Cluster‐based resource allocation scheme with QoS guarantee in ultra‐dense networks

Ultra-dense networks (UDN) can provide extremely high throughput and data rate. However, there are severe interference due to dense and random deployment of femto base stations (FBSs). To mitigate interference and allocate network resource efficiently while ensuring quality of service (QoS) of user equipments (UEs), a cluster-based resource allocation scheme for UDN is proposed in this paper. Two stages, clustering and resource allocation, are involved in the scheme. In clustering stage, a modified K-means clustering algorithm is advanced to divide FBSs into different disjoint clusters dynamically according to the density of FBSs. Thus the number of clusters can be adjusted flexibly to fit for the dynamic network topology. In resource allocation stage, a greedy-based compensatory resource allocation algorithm (GCRAA) is further proposed to maximize the throughput of UDN. Herein the orthogonal resource blocks (RBs) are initially assigned among the UEs with a greedy algorithm. In order to ensure the fairness and QoS of UEs, a compensatory resource allocation algorithm is further proposed to allocate the remaining RBs. The simulated results show that the proposed resource allocation scheme can mitigate the interference in UDN effectively, and improve the system throughput while ensuring the QoS for UEs.

A Linear Objective Function-Based Heuristic for Robotic Exploration of Unknown Polygonal Environments

This work presents a heuristic for describing the next best view location for an autonomous agent exploring an unknown environment. The approach considers each robot as a point mass with omnidirectional and unrestricted vision of the environment and line-of-sight communication operating in a polygonal environment which may contain holes. The number of robots in the team is always sufficient for full visual coverage of the space. The technique employed falls in the category of distributed visibility-based deployment algorithms which seek to segment the space based on each agent’s field of view with the goal of deploying each agent into the environment to create a visually connected series of agents which fully observe the previously unknown region. The contributions made to this field are a technique for utilizing linear programming methods to determine the solution to the next best observation (NBO) problem as well as a method for calculating multiple NBO points simultaneously. Both contributions are incorporated into an algorithm and deployed in a simulated environment built with MATLAB for testing. The algorithm successfully deployed agents into polygons which may contain holes. The efficiency of the deployment method was compared with random deployment methods to establish a performance metric for the proposed tactic. It was shown that the heuristic presented in this work performs better the other tested strategies.

Monitoring tropical forest ungulates using camera‐trap data

AbstractTropical forest ungulates are poorly known and, despite their ecological and conservation significance, there are few studies on their density and abundance. I estimated densities of lesser oriental chevrotain Tragulus kanchil in the Southern Cardamom National Park, south‐west Cambodia using camera‐trap encounter rates and fitting a random encounter model (REM) that does not require individual identification of animals. This represents the first use of REM on forest ungulates in mainland tropical Asia and provides the first density estimate for any chevrotain species in mainland South East Asia. Sixty‐five camera‐trap stations generated 501 encounters of lesser oriental chevrotain across >8200 camera‐trap nights within a systematic random camera‐trap grid of 200 km2 in the Southern Cardamom National Park. Density of lesser oriental chevrotain was estimated at between 57 and 98 ( 81) individuals per km2. The random deployment of camera‐traps, a prerequisite of the REM, did not prevent the detection of the majority of ground‐dwelling large mammal species likely to be present in the study site. Despite its slow uptake by field conservationists, the REM may have potential for monitoring tropical ungulates particularly in dense evergreen forest where other methodologies, for example, distance‐based line transect sampling are unsuitable.

Energy-Efficient Deployment of Relay Nodes in Wireless Sensor Networks Using Evolutionary Techniques

Random deployment of sensor nodes is susceptible to initial communication hole, even when the network is densely populated. However, eliminating holes using structural deployment poses its difficulties. In either case, the resulting coverage holes can degrade overall network performance and lifetime. Many solutions utilizing Relay Nodes (RNs) have been proposed to alleviate this problem. In this regard, one of the recent solutions proposed using Artificial Bee Colony (ABC) to deploy RNs. This paper proposes RN deployment using two other evolutionary techniques - Gravitational Search Algorithm (GSA) and Differential Evolution (DE) and compares them with existing solution that uses ABC. These popular optimization tools are deployed to optimize the positions of relay nodes for lifetime maximization. Proposed algorithms guarantee satisfactory RNs utilization while maintaining desired connectivity level. It is shown that DE-based deployment improves the network lifetime better than other optimization heuristics considered.

Design and modeling of survivable network planning for software‐defined data center networks in smart city

SummarySmart city can integrate cloud computing, big data, Internet of Things, edge computing, and other modern information technologies, which focuses on integration, sharing, utilization, and service of information resources, and emphasizes the cooperation and coordination of urban management, to achieve deep integration of industrialization and informatization. Data center network planning based on big data is involved in rational deployment and interconnection of network infrastructures, which can improve the network survivability, energy‐efficiency, flexibility, etc. Therefore, a survivable network planning model based on software‐defined networking is presented in this paper. The elastic network planning problem is formulated as a mixed‐integer optimization whose objective is to minimize the number of unprotected nodes. Then, 3 effective schemes including K‐means clustering algorithm based on simulated annealing, greedy routing algorithm, and Lagrangian relaxation algorithm are proposed for feasible solutions. Numerical results on practical network topology reveals that (1) K‐means clustering algorithm based on simulated annealing can make effective classification of different scale topologies; (2) compared with the traditional random deployment method, the greedy routing algorithm and Lagrangian relaxation algorithm can generate the controller deployment strategy more reasonably, and thus improve network survivability and deployment cost‐benefit.

ODA: Optimal Deployment Algorithm for Wireless Sensor Network for Coverage Enhancement

Wireless sensor network (WSN) became the topic of interest in past decades. Wireless sensor networks used to get the information of surroundings. WSN are used mainly in the areas where person cannot reach. It can be used in forests to detect fire, underwater to detect seismic activities, in defence to detect activities of enemies etc. As we said WSN is used to detect activities in unreachable areas. So the basic issue occurs here is regarding the deployment of nodes and coverage of sensing area. The most basic form of deployment is the random deployment. Which result in the unequal distribution of the Sensor nodes. This also leads to the holes in the coverage area. Efficient node deployment is the major requirement in the working of WSN. If any algorithm is not able to do whole area coverage then it will affect the routing techniques as well. One challenge in working with WSN is low energy consumption. We have proposed one algorithm in this paper having two steps, doing random deployment followed by distribution of nodes in equal partitions. The simulation based performance evaluations of the proposed algorithms are carried out in terms of area coverage, convergence rate, and energy efficiency. Compared to the existing works, ODA improved area coverage with faster convergence. It is concluded that the proposed algorithm has improved area coverage with faster convergence and minimal energy consumption.

Wireless Sensor Networks: Attacks and Countermeasures

Wireless sensor networks (WSNs) are widely used in different operations like water quality monitoring, military, land sliding detection, health monitoring, fire detection and target tracking and so on. The nature of information collected by the sensor nodes makes security major concern in WSNs. Comparing to the traditional networks; wireless sensor networks have limitations like low energy, storage capacity and computational capability, etc. On the other hand, its random deployment, unattended operations and unreliable communication make it more susceptible to various types of attacks. The design and development of simple, secure and lightweight security mechanism must be required at every step of the communication in WSNs. This paper describes challenges, security requirements, and different types of attacks. The security mechanisms and related work are also discussed.

SAL：无线传感器网络中信号强度辅助的定位算法

针对现有的测距无关定位算法不能在定位精度和定位开销之间较好平衡的问题，提出了信号强度辅助的定位算法SAL (Signal Strength Assisted Localization)，充分利用无线电接收信号强度随距离定性衰减的规律，对单跳范围内的锚节点进行更细粒度的区分，并将距离远近信息应用于待定位节点的位置估计。在不需要额外硬件、不增加通信开销的情况下有效提高了节点的定位精度，并确保网络定位覆盖率为100%。大量仿真和平台现场试验表明，SAL兼具质心法实现简单和APIT定位精度高的优势，在随机大规模仿真和现场试验中SAL的定位精度均高于APIT；在平均情况下，SAL的计算开销至多仅为APIT的1/4。 To solve the problem of current range-free localization algorithms in failing to balance localization precision and localization cost well, Signal Strength Assisted Localization (SAL) algorithm is proposed. SAL takes advantage of the discipline that radio signal strength (RSS) monotonically decreased qualitatively as physical distance increasing in open outdoor environment. SAL distinguishes 1-hop anchors into sub-hop resolution beyond connectivity and utilizes this heuristic in-formation about which anchor is closer and which is further to associate location estimation of unknown nodes. Without increasing additional hardware and communication cost, the localization precision is improved effectively and network localization coverage ratio is guaranteed to be 100%. Extensive simulations and platform experiment show that, SAL possesses the advantages of simple implementation of centroid algorithm and high localization precision of APIT. Under random deployment, the localization precision of SAL is better than APIT and the computational cost of SAL is less than a quarter of APIT on average.

Optimal mobile beacon trajectories for nodes localisation in wireless sensor networks

The random deployment of nodes is commonly used in wireless sensor networks (WSNs), because of either the hostility of the monitored area or its large scale. However, many applications and protocols are position-based. Hence, it's necessary to determine the position of sensors even after their deployment. Localisation techniques using mobile beacon have been proposed in this context, to localise unknown sensor nodes. However, to save nodes energy it's useful to send an optimal number of messages. Thus, an optimal trajectory of mobile beacon helps to achieve such objective. In this paper, we propose two novel optimal mobile beacon trajectories based on Hilbert curve. The first proposal aims at minimising the trajectory length and improving the localisation accuracy, moreover, the second one minimises both the trajectory and the energy consumption. In this study, we compare the proposed techniques with mobile Hilbert beacon trajectory. The analytical analysis and the performance evaluation with simulations show that our proposed methods improve, compared to the existing methods, the accuracy, the length of trajectories and the energy consumption.

FHC-PCIA: A Physical Cell Identification Allocation Method Based on Fuzzy Hierarchical Clustering for Heterogeneous Cellular Network

When users seek for Network in a heterogeneous cellular network, physical cell identification (PCI) is used to distinguish different cellular cells. However, the number of PCIs is extremely limited. Moreover, they are unable to satisfy large-scale random deployment of small cellular cells as well as difficult to guarantee users’ quality of service. Therefore, this paper presents a fuzzy hierarchical clustering-physical cell identification allocation scheme. The proposal is based on the “activity” of cellular base station (CBS) and uses the Euclidean distance method to obtain the degree of similarities between the CBSs. After dynamic fuzzy clustering, the optimal clustering results are obtained by using the analysis of variance. In this paper, high priority of allocating and reusing the PCIs are given to those CBSs with higher activity. Simulation results show a clear increase in PCI allocation efficiency and reduction in PCI-conflict and PCI-confusion possibilities when compared with the existing schemes.

A Novel Heterogeneous Wireless Sensor Node Deployment Algorithm With Parameter-Free Configuration

The deployment of wireless sensor nodes affects the WSN performance, such as the sensing coverage, the connectivity between sensor nodes, and the network lifetime. The deployment is much more complicated when the sensor nodes are heterogeneous and there are some environmental obstacles in an area of interest. The deployment of heterogeneous sensor nodes in an area of interest with obstacles is a challenging problem and also proven to be an NP-complete problem. This paper proposes and evaluates a wireless sensor node deployment algorithm, called DeVForce-AP . The proposed algorithm combines the benefits of the two well-known deployment algorithms, Delaunay triangulation method (D) and extended virtual force algorithm (eVForce), with the adaptive parameter (AP) tuning mechanism. The DeVForce-AP attempts to improve the sensing coverage and network lifetime of the heterogeneous wireless sensor node. The practical deployment problems, such as the environmental obstacles, the network connectivity, and the sensing coverage area, have also been addressed. The DeVForce-AP consists of two steps. In the first step, the DeVForce-AP applies the Delaunay triangulation method to find and eliminate the largest coverage hole in the area of interest. In the second step, the DeVForce-AP applies eVForce method to avoid the existence of environmental obstacles and maintain the network connectivity. Moreover, the proposed DeVForce-AP automatically adjusts its parameters to the environmental scenario. Thus, there is no need to configure the parameters of the DeVForce-AP. The simulation results show that the proposed DeVForce-AP outperforms the random deployment, the traditional Delaunay triangulation deployment, and the traditional virtual force deployment methods.

Optimal mobile beacon trajectories for nodes localisation in wireless sensor networks

The random deployment of nodes is commonly used in wireless sensor networks (WSNs), because of either the hostility of the monitored area or its large scale. However, many applications and protocols are position-based. Hence, it's necessary to determine the position of sensors even after their deployment. Localisation techniques using mobile beacon have been proposed in this context, to localise unknown sensor nodes. However, to save nodes energy it's useful to send an optimal number of messages. Thus, an optimal trajectory of mobile beacon helps to achieve such objective. In this paper, we propose two novel optimal mobile beacon trajectories based on Hilbert curve. The first proposal aims at minimising the trajectory length and improving the localisation accuracy, moreover, the second one minimises both the trajectory and the energy consumption. In this study, we compare the proposed techniques with mobile Hilbert beacon trajectory. The analytical analysis and the performance evaluation with simulations show that our proposed methods improve, compared to the existing methods, the accuracy, the length of trajectories and the energy consumption.

An Energy Efficient Autonomous Method for Coverage Optimization in Wireless Multimedia Sensor Networks

In this paper, a distributed method for coverage optimization of random deployed WMSNs utilizing motility and mobility capabilities of nodes, is proposed. The aims followed by the method are first, maximizing the coverage ratio by minimizing both the covered overlapping areas, and the coverage holes after random deployment, and second, enhancing energy efficiency of the coverage optimization procedure, by minimizing the needed rotations and specially movements, comparing with the previous schemes. To these aims, the most appropriate location and orientation of the nodes are calculated round by round considering all the possible nested compositions of rotation and movement. But, rotating and moving the nodes are performed after terminating the algorithm rounds and achieving the decisive results. So, the proposed method does not impose the overhead of trial and error of rotation or relocation on the network nodes. The performance of the proposed approach has been compared with the previous works for different network configurations; simulation results show that the proposed approach outperforms the previous schemes in terms of both coverage ratio and energy efficiency.

Base Station Sleep and Spectrum Allocation in Heterogeneous Ultra-dense Networks

To meet the exponential increasing high data rate demand of mobile users, heterogeneous ultra-dense networks (UDN) is widely seen as an essential technology to provide high-rate transmissions to nearby mobile users. However, the dense and random deployment of small base stations (SBSs) overlaid by macro base stations and their uncoordinated operation lead to important questions about the power consumption and aggressive frequency reuse of heterogeneous UDN. For the problem of huge power consumption and spectrum resource tension in heterogeneous UDN, a joint strategy of SBSs sleep and spectrum allocation is proposed. By using stochastic geometry, the coverage probabilities of base stations and the average ergodic rates of mobile users are derived in each tier and the whole network. In addition, we formulate the coverage probability maximization and power consumption minimization problems, and determine the optimal operating regimes for SBSs, and as well as spectrum allocation. The numerical results show that the SBSs sleep and spectrum allocation can reduce the power consumption and interference of the whole network.

Improved DV-Hop Algorithm Using Locally Weighted Linear Regression in Anisotropic Wireless Sensor Networks

The original DV-hop algorithm performs pretty well in isotropic Wireless Sensor Networks in which nodes distribute uniformly. However, the localization accuracy degrades severely in anisotropic networks caused by uneven nodal distribution or irregularity of deployment region. In this paper, we propose a novel DV-hop algorithm based on Locally Weighted Linear Regression (LWLR-DV-hop), in which kernel method is adopted to improve the localization accuracy by raising the weight of neighboring anchor nodes. In the simulation section, algorithms are evaluated within two deployments and three topologies: the regular and random deployments, the L-shaped, O-shaped and X-shaped topologies. As performance metrics, the Average Localization Error and the Cumulative Distribution Function are used. The results of simulation and experiment reveal that LWLR-DV-hop performs better than original DV-Hop in anisotropic networks of different topologies, in which localization accuracy is improved by about 40% on average.

Handover Management in Two-Tier Femtocell–Macrocell Network

Good mobile coverage and better data rate is key to customer satisfaction. For latest 4G and 5G technologies as well 5 bar mobile coverage is required, to perform at its best. Small cell technology like femtocell helps these latest mobile communication technologies to achieve their goal of better coverage and high data rate. Femto cells are low cost, low power and efficient way to achieve better coverage. It has ability to boost mobile signal in indoor or in areas with little or no mobile coverage. For successful deployment of femto cells in predominant macrocell network seamless communication in between these two is must. For uninterrupted communication, handover should be carefully handled between femtocell and macrocell when user is moving around femtocell. Handover management is quite challenging in integrated macrocell and femtocell network because of random deployment of femtocell in planned macrocell network. In this article, we have proposed new handover approach with call admission control for uninterrupted communication between femto cell and macrocell network. This approach also helps to avoid unnecessary handovers due to small coverage area of femtocells.

Fairness constrained diffusion adaptive power control for dense small cell network

Small cell is an emerging and promising technology for improving hotspots coverage and capacity, which tends to be densely deployed in populated areas. However, in a dense small cell network, the performances of users differ vastly due to the random deployments and the interferences. To guarantee fair performance among users in different cells, we propose a new distributed strategy for fairness constrained power control, referred to as the diffusion adaptive power control (DAPC). DAPC achieves overall network fairness in a distributed manner, in which each base station optimizes a local fairness with little information exchanged with neighboring cells. We study several adaptive algorithms to implement the proposed DAPC strategy. To improve the efficiency of the standard least mean square algorithm (LMS), we derive an adaptive step-size logarithm LMS algorithm, and discuss its convergence properties. Simulation results confirm the efficiency of the proposed methods.