Density Of Sensor Nodes Research Articles

Clustering Uniformity Methods for Energy Efficiency in Wireless Sensor Networks

The wireless sensors that make up a wireless sensor network (WSN) are randomly deployed in nature and cannot be artificially replaced when their batteries are depleted. Failure of communication connection between wireless sensors causes continuous connection attempts, which results in a lot of power dissipation and shortens the lifetime of the WSN. In this paper, we propose to extend the lifetime of WSNs by limiting the appropriate distance between the cluster head (CH) node and the communicating sensor nodes (SNs) so that a group of clusters of appropriate size can be formed on a two-dimensional plane. To equalize cluster size, sensor nodes with the shortest distance communicate with each other to form member nodes, and nodes with closer distances are bring together to form clusters. The simulation results show the improvement rate of cluster uniformity over the shortest distance-based clustering method for clustering based on the proposed cluster uniformity algorithm. The proposed method can improve the cluster uniformity of the network by about 20%. In addition, the power consumption of the proposed method is analyzed according to the difference in the density of sensor nodes in the cluster groups to examine the improvement in power consumption.

Multiparticle collision dynamics simulations of hydrodynamic interactions in colloidal suspensions: How well does the discrete particle approach do at short range?

The multiparticle collision dynamics (MPCD) simulation method is an attractive technique for studying the effects of hydrodynamic interactions in colloidal suspensions because of its flexibility, computational efficiency, and ease of implementation. Here, we analyze an extension of the basic MPCD method in which colloidal particles are discretized with a surface mesh of sensor nodes/particles that interact with solvent particles (MPCD + Discrete Particle or MPCD + DP). We use several situations that have been described analytically to probe the impact of colloidal particle mesh resolution on the ability of the MPCD + DP method to resolve short-ranged hydrodynamic interactions, which are important in crowded suspensions and especially in self-assembling systems that create high volume fraction phases. Specifically, we consider (A) hard-sphere diffusion near a wall, (B) two-particle diffusion, (C) hard-sphere diffusion in crowded suspensions, and (D) the dynamics of aggregation in an attractive colloidal suspension. We show that in each case, the density of sensor nodes plays a significant role in the accuracy of the simulation and that a surprisingly high number of surface nodes are needed to fully capture hydrodynamic interactions.

A NEW APPROACH TO WIRELESS CHARGING AND ON-DEMAND DATA GATHERING (SSWCODDG) FOR RWSN

These days, WSNs (wireless sensor networks) are all about collecting data and reducing energy use. Mobile sinks and chargers can visit Cluster Heads (CHs) to gather data and recharge their batteries, respectively. A mobile VAN (MV) combines a mobile charger and sinks to improve the energy efficiency of a WSN. Determining the CHs’ guest instructions for wireless data collecting and charging is difficult. In response to these problems, the authors of this study present SSWCODDG (Santosh Soni Wireless Charging and on Demand Data Gathering), a new approach to wirelessly charging and data collection for rechargeable wireless sensor networks. Utilizing mobile VAN, the proposed algorithm gathers data from CHs and charges them according to their choices and needs. Here, Mobile VAN maintains a routing table and, in response to requests, visits CHs. To divide the network into clusters, the proposed method employs the LEACH clustering algorithm. One full cycle is executed by the LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm. To back up its claims, this study compares the recently created SSWCODDG algorithm to the SPSS and M2C algorithms that came before it. In addition, SSWCODDG outperforms SPSS (Single path scheduling scheme) and M2C (Mobile to Cluster) for a range of sensor node densities by 38.62 and 25.78 percentage points, respectively.

Enhanced Network QoS in Large Scale and High Sensor Node Density Wireless Sensor Networks Using (IR-DV-Hop) Localization Algorithm and Mobile Data Collector (MDC)

Enhanced Network QoS in Large Scale and High Sensor Node Density Wireless Sensor Networks Using (IR-DV-Hop) Localization Algorithm and Mobile Data Collector (MDC)

An efficient 3D localization algorithm for compensating stratification effect in underwater acoustic sensor network

AbstractCommunication and networking plays a crucial role in data transmission in UASN. The sensor nodes in UASN are intermittently deployed randomly in the three‐dimension scenario. As a consequence, the three‐dimension based localization algorithm is the need for an hour. To fulfill the objective, we have proposed a localization algorithm I‐LASP (Improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) in three‐dimensional UASN, based on compensation of stratification effect for the improvement of the performance parameters such as localization accuracy, ranging accuracy, convergence rate and execution time. To compute the accurate position of target nodes, EIPSO (Extended improved PSO) technique is applied, and the degree of coplanarity is checked before the calculation of distance among nodes in order to get the accurate location of target nodes. The Centroid method is used to initialize the position of sensor nodes, and the ray theory method is used to compensate the stratification effect on the layered ocean water. The proposed algorithm is compared to the existing LASP, Std PSO, and GNA‐ESSP (Gauss‐newton algorithm‐extended sound speed profile) localization algorithm. The proposed algorithm provides 34.50%, 38.87%, and 42.66% of high accuracy in terms of localization with low density of target sensor nodes and 37.96%, 29.58%, and 50.77% high accuracy in terms of localization with a high density of target sensor nodes respectively. The proposed algorithm is compared with LASP, GNA‐ESSP, and TDOA to obtain 66.84%, 71.14%, and 86.13% of high accuracy in terms of ranging with low density of target sensor nodes and 42.34%, 89.00%, and 95.08% high accuracy in terms of ranging with a high density of target sensor nodes respectively. Experimental results represents that the proposed algorithm obtains better performance in terms of localization accuracy, ranging accuracy, root mean square error, normalized localization error, execution time and convergence rate.

Energy-Efficient Directional Charging Strategy for Wireless Rechargeable Sensor Networks

Mobile chargers (MCs) equipped with radio-frequency (RF)-based wireless power transfer (WPT) modules have been suggested as a possible solution to battery constraints in wireless rechargeable sensor networks (WRSNs). In RF-based WPT, charging efficiency decreases significantly as the charging distance increases. Therefore, single charging consumes less energy than multicharging because it can generally charge a sensor node at a closer range. However, when the density of nodes is high, multicharging may achieve higher efficiency. We propose an energy-efficient adaptive directional charging (EEADC) algorithm that considers the density of sensor nodes to adaptively choose single charging or multicharging. The EEADC exploits directional antennas to concentrate the energy and improve energy efficiency and identifies the optimum charging points and beam directions to minimize energy consumption. In the EEADC, clustering is performed by considering the density of the sensor nodes. After clustering, the clusters are classified into single-charging/multicharging clusters according to the number of sensor nodes in each cluster. Next, the charging strategy is determined according to the type of cluster. In the case of a multicharging cluster, the problem is nonconvex. Therefore, a discretized charging strategy decision (DCSD) algorithm is proposed. The performance evaluation indicates that EEADC outperforms two existing methods in terms of power consumption and charging delay by 10% and 9%, respectively.

An improved Aquila optimization with fuzzy model based energy efficient cluster routing protocol for wireless sensor networks

Due to the latest advances in microelectronics, wireless sensor networking (WSN) has been introduced in many applications. The flow of event data in WSN applications requires timely and reliable distribution so that immediate response and appropriate action can be taken. However, the limited power supply to the sensor terminal causes a transmission between the delay on the way to the base station and the power consumption. Clustering techniques are essential in developing the WSN routing algorithm that improves network operating time and power efficiency. However, due to the unbalanced power consumption between the terminals, the WSN is the optimal, energy efficient routing. In this work, we propose an energy efficient cluster based routing protocol (EEC-HO) for WSN using hybrid optimization algorithm. We introduce an improved Aquila optimization with fuzzy (IAO-Fuzzy) model for optimal and efficient cluster formation and cluster head (CH) computation. The main objective of proposed IAO-Fuzzy model used to compute the trust degree of each node, the highest trust node is considered as CH. After that, the hybrid beetle search induced decision making (BSDM) algorithm for optimal path selection to transfer data transfer between two nodes. Finally, the simulation results of proposed EEC-HO routing protocol is compared with the existing routing protocols. For the impact of sensor node density case, we observed that the effectiveness of proposed EEC-HO routing protocol is 45.887%, 30.666%, 56.666%, 17.629% and 41.666% efficient than existing protocols in terms of energy consumption, network lifetime, average hop count, throughput and dead nodes respectively. For the impact of simulation rounds case, we observed that the effectiveness of proposed EEC-HO routing protocol is 21.216%, 35.417%, 41.667%, 18.568% and 40.000% efficient than existing protocols in terms of energy consumption, network lifetime, average hop count, throughput and number of dead nodes respectively.

A Novel Efficient Data Gathering Algorithm for Disconnected Sensor Networks Based on Mobile Edge Computing

Employing mobile elements is an efficient solution to the performance improvement of wireless sensor networks (WSNs). We propose an efficient data gathering mechanism for disconnected WSNs with rendezvous points (DGM-RPs). The mobile sink traverses the entire network and stops only at the rendezvous points (RPs) while gathers the data from sensors in every disconnected segment. In this paper, mobile sinks perform the task of edge computing and alleviate the load of upper cloud. We measure the shape of disconnected segments, layering them by use of the convex hull, and then design the travelling path of the mobile sink to minimize the travel latency to visit all disconnected segments. At least one RPs will be selected in a segment firstly, and then, on this basis, we consider the distribution density of sensor nodes and the location of the RPs already exist to adding new RPs, which make good use of the margin to reducing the energy consumption and prolong the network lifetime.

Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks.

The seamless operation of inter-connected smart devices in Internet of Things (IoT) wireless sensor networks (WSNs) requires consistently available end-to-end routes. However, the sensor nodes that rely on a very limited power source tend to cause disconnection in multi-hop routes due to power shortages in the WSNs, which eventually results in the inefficiency of the overall IoT network. In addition, the density of the available sensor nodes affects the existence of feasible routes and the level of path multiplicity in the WSNs. Therefore, an efficient routing mechanism is expected to extend the lifetime of the WSNs by adaptively selecting the best routes for the data transfer between interconnected IoT devices. In this work, we propose a novel routing mechanism to balance the energy consumption among all the nodes and elongate the WSN lifetime, which introduces a score value assigned to each node along a path as the combination of evaluation metrics. Specifically, the scoring scheme considers the information of the node density at a certain area and the node energy levels in order to represent the importance of individual nodes in the routes. Furthermore, our routing mechanism allows for incorporating non-cooperative nodes. The simulation results show that the proposed work gives comparatively better results than some other experimented protocols.

Simulated study of the influence of node density on the performance of wireless sensor networks

This paper investigates the impact of local and global node density in cluster-based structured wireless sensor networks (WSNs). The local density represents sensor node density (SND) in a cluster whereas, global node density relates to head node density (HND) in the entire WSN. The literature rarely addresses the impact of density on WSNs performance as the focus is typically on protocols, routing, scheduling, clustering and network longevity. Often, the density of nodes is assumed heuristically, but not based on empirical experiments. In this work, we address this issue by measuring the impact of node density on four performance metrics: isolated sensor nodes, isolated head nodes, network detection effectiveness and network tracking accuracy. Using an in-house simulator, a total of 5,200 experiments were conducted and performance-metrics were collected and analysed. The results revealed interesting relationships among the studied variables and identified best performing node densities locally and globally.

A Novel Area Coverage Technique for Maximizing the Wireless Sensor Network Lifetime

This paper presents a mathematical model to geometrically optimize the density of active sensor nodes in a wireless sensor network (WSN) using concentric hexagonal tessellations and the concept of coverage contribution area for randomly deployed nodes in the field of interest (FOI). Some of the WSN applications, such as environmental monitoring, security, surveillance and health care require the target area to be covered a number of times. This number is denoted by a variable $$k$$ and is known as the ‘degree of coverage.’ The problem of achieving required degree of coverage is formulated as $$k$$ -coverage problem. An algorithm has been proposed to generate maximum number of disjoint-independent subsets of sensor nodes as an optimized solution to the $$k$$ -coverage problem, along with maximizing the WSN lifetime. Superiority and efficacy of the technique have been verified by mathematical analysis as well as simulations carried out using MATLAB.

Optimal Recharge Scheduler for Drone-to-Sensor Wireless Power Transfer

Wireless recharging by autonomous power delivery vehicles is an attractive maintenance solution for Internet of Things devices. Improving the operating efficiency of power delivery vehicles is challenging due to complex dynamic environments and the need to solve difficult optimization problems to determine the best combination of routes, number of vehicles, and numerous safety thresholds prior to deployment. The optimal recharge scheduling problem considers minimizing discharged energy of drones while maximizing devices’ recharged energy. In this paper, a configurable optimal recharge scheduler is proposed that incorporates several evolutionary and clustering approaches. A modified version of the Black Hole algorithm is presented, which is shown to execute on average 35% faster than the state of the art genetic approach, while delivering comparable performance in simulation across 18 scenarios with varying area and density of sensor nodes deployed under different initialization scenarios.

Spatial Correlation Based Clustering with dynamic CH Selection Scheme towards Enhanced QoS Metrics in Wireless Sensor Networks

Resource constrained wireless sensor nodes are generally randomly distributed in a given area of interest to sense required information and the sensed data is transmitted to the sink station or Base Station (BS) through various clustering and data routing algorithms. The standard clustering algorithms that are aimed at efficient data routing techniques are Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm , Distributed Energy Efficient Clustering (DEEC) algorithm, Stable Election Protocol (SEP) and various others. Most of these algorithms are the various variants of LEACH. Our proposed scheme of data gathering and routing is based on a two hop structure wherein the Cluster Leader (CL) forwards the aggregated received data from the Cluster members to the Sink directly. The Chance-value that determines the Cluster Leader in a round is decided by a combination of parameters specific to the Sensor nodes in the cluster. In our simulation approach we have also tried to analyze the effect of changing density of sensor nodes in the select area. Thus in our proposed scheme, we embark on a fixed Clustering scheme where the CL is selected dynamically so as to extend the network’s lifetime and achieve enhanced throughput in comparison to the standard algorithms like DEEC, SEP and LEACH.

Analytic evaluation of non-uniformities for coverage probability computation of randomly deployed wireless sensor network

The border region of a country is almost a remote hostile geographic region which requires continuous surveillance to prevent from unauthorised intrusion. Sensor nodes can be deployed randomly in such regions for surveillance. The quality of surveillance is measured by coverage rate. This application imposes many non-uniformities for coverage rate computation such as asymmetrical locations, irregular sensing range and biased coverage due to coverage redundancy and coverage holes. This paper proposes a coverage probability computation (CPC) protocol that considers these non-uniformities while computing coverage rate. CPC computes the distribution of coverage probabilities within the sensing region of each sensor node using its probabilistic sensing range. This paper also derives the lower bound for a minimum number of sensor nodes required to cover the hostile region. The simulation results of CPC show that at an optimum density of sensor nodes the hostile region can be covered up to a threshold level.

Design of coverage algorithm for mobile sensor networks based on virtual molecular force

General virtual force algorithm, when the density of sensor nodes is large in the monitoring area, there are some shortcomings, such as uneven distribution of nodes, more overlap of coverage area and so on. In view of these shortcomings, based on the basic idea of air molecular theory, a virtual molecular force model of mobile sensor networks is established, and the virtual molecular force algorithm for node deployment and mobile coverage of mobile sensor networks is given. The virtual molecular force algorithm assumes that there is interaction between nodes in mobile sensor networks, and the resultant force of these forces constitutes the resultant force network of sensor nodes in the monitoring area, which drives the sensor nodes to move to the corresponding location to repair the monitoring blind area, so as to maximize the coverage of the network. In order to verify the feasibility and effectiveness of the virtual molecular force algorithm, the virtual molecular force algorithm for mobile sensor network node deployment and mobile coverage is simulated and analysed by using MATLAB simulation tool. The simulation results show that the virtual molecular force algorithm can make the sensor nodes repair the monitoring blind area efficiently and quickly, and maximize the monitoring coverage area of the sensor network. In terms of repairing monitoring blind areas and improving network coverage, the virtual molecular force algorithm is superior to other network coverage algorithms such as general virtual force algorithm.

An Enhanced Cluster Head Selection of LEACH Based on Power Consumption and Density of Sensor Nodes in Wireless Sensor Networks

The cluster head of LEACH algorithm was selected randomly in WSNs. It effects the distribution of clusters for the shadowing effect of obstruction in real scenarios. The network consumption is increased because of poor communication between nodes. A new selection method is presented to solve those problems in this paper. The logarithmic function is adopted to eliminate the shadowing effect of obstruction. The most suitable cluster is sorted out because the density of nodes is defined as the new threshold value. Simulation results show that the performances of new algorithm are obviously better than LEACH, ALEACH and Kost-LEACH algorithms. Compared with classical algorithms, the nodes utilization could enhance more than 2.0%, average energy consumption of the nodes is reduced by 9.1 J at least, and the probability of nodes failure to join the cluster could be decreased great than 3.7%.

Clustering and Compressive Data Gathering in Wireless Sensor Network

In wireless sensor network (WSN) redundant data gathering and transmission occurs due to dense deployment. Recently compressive sensing (CS) has attracted considerable attention for efficient data gathering in WSN. CS can reduce data transmission but the total number of transmissions for data collection is high. To alleviate this, hybrid of CS and raw data collection is proposed and integrated with clustering. Clusters used in this integration reduce the number of CS transmissions, but do not reduce the number of transmissions. In a cluster amount of transmission depends on the number of transmitting nodes and their location in data gathering, hence the way in which nodes are clustered together can significantly effect on the number of transmissions in cluster and overall transmissions in network. When density of sensor nodes in a network is high, we can take advantage of their inherent spatial correlation to reduce the number of transmissions. Motivated by this, we propose a novel base station (BS) assisted cluster, spatially correlated, to reduce the number of transmission in a CS-based clustered WSN. Different from other spatially correlated clusters, in this cluster only CH senses, gathers data in the correlated region, and then transmits compressively sensed measurements to BS without incurring any intra-cluster communication cost. In addition, the clusters so formed, convert a randomly deployed network into a structured one i.e. when several clusters are grouped together they form a hexagonal topology (proved to have a high success rate in cellular network). The proposed system makes WSN transmission efficient by reducing number of transmissions in the network and number of data transmission at the CH using clustering and CS. Also energy consumption is reduced and network lifetime is prolonged.

A coverage-aware and energy-efficient protocol for the distributed wireless sensor networks

Wireless sensor network (WSNs) are composed of a large number of battery-powered wireless sensors, which acquire and monitor physical data from their surroundings through self-organization. The sensors are deployed randomly in a target area where maintenance and battery replacement are difficult or even impossible. To achieve better coverage and prolong network lifetime, networks typically adopt clustering protocols with hierarchical inter-cluster topology for network management and data acquisition in WSNs. However, typical solutions require cluster re-configuration due to early death of cluster heads (CHs) and cause energy inefficiency. This paper proposes a coverage- and energy-aware protocol with intra- and inter-cluster methods called CEMST that considers the sensor node density and coverage overlapping. In addition, to adapt network dynamics while improving energy efficiency, self-stabilizing algorithm and Borůvka algorithm are applied to construct the minimum spanning trees (MST) for intra- and inter-cluster routes, respectively. Simulation results indicate that CEMST produces the balanced clustering structures and provides better coverage and longer network lifetime than previous methods.

Detection Accuracy of Soccer Players in Aerial Images Captured from Several Viewpoints.

In the fields of professional and amateur sports, players’ health, physical and physiological conditions during exercise should be properly monitored and managed. The authors of this paper previously proposed a real-time vital-sign monitoring system for players using a wireless multi-hop sensor network that transmits their vital data. However, existing routing schemes based on the received signal strength indicator or global positioning system do not work well, because of the high speeds and the density of sensor nodes attached to players. To solve this problem, we proposed a novel scheme, image-assisted routing (IAR), which estimates the locations of sensor nodes using images captured from cameras mounted on unmanned aerial vehicles. However, it is not clear where the best viewpoints are for aerial player detection. In this study, the authors investigated detection accuracy from several viewpoints using an aerial-image dataset generated with computer graphics. Experimental results show that the detection accuracy was best when the viewpoints were slightly distant from just above the center of the field. In the best case, the detection accuracy was very good: 0.005524 miss rate at 0.01 false positive-per-image. These results are informative for player detection using aerial images and can facilitate to realize IAR.

Repairing Confident Information Coverage Holes for Big Data Collection in Large-Scale Heterogeneous Wireless Sensor Networks

The quality of service (QoS) and lifetime of wireless sensor networks (WSNs) are severely degraded by coverage holes generated by random deployment or battery exhaustion of sensors. This work firstly introduces a novel confident information coverage CIC) model to dramatically reduce the density of sensor nodes and accurately detect confident information coverage holes (CICHs). Then, the problem of repairing confident information coverage holes (RCICHs) for big data collection in a large-scale heterogeneous WSN (LS-HWSN) which widely spreads over a geographic area with thousands of stationary sensor nodes and mobile sensor nodes is formulated, called as RCICH problem, which is to effectively repair CICHs considering that the transmitted data velocity of sensor nodes is different. Furthermore, we prove it to be NP-completeness. The target of the problem is to find a subset of mobile sensor nodes from all mobile sensor nodes while minimizing the amount of lost throughputs LTs) of all dispatched mobile sensor nodes or maximizing the amount of repairing transmission times (RTTs) for all dispatched mobile sensor nodes, with different objectives. Finally, based on the CIC model and the data-centric perspective, two heuristic schemes including a centralized dispatch scheme and a distributed dispatch scheme are proposed to effectively solve the RCICH problem. Simulation results show that the proposed schemes effectively repair CICHs while increasing the QoS and lifetime of the LS-HWSN with the topology control of a fan-shaped clustering (FSC) protocol.