Performance evaluation of two-fuzzy based cluster head selection systems for wireless sensor networks

Sensor networks supported by recent technological advances in low power wireless communications along with silicon integration of various functionalities are emerging as a critically important computer class that enable novel and low cost applications. There are many fundamental problems that sensor networks research will have to address in order to ensure a reasonable degree of cost and system quality. Cluster formation and cluster head selection are important problems in sensor network applications and can drastically affect the network’s communication energy dissipation. However, selecting of the cluster head is not easy in different environments which may have different characteristics. In this paper, in order to deal with this problem, we propose two fuzzy-based systems for cluster head selection in sensor networks. We call these systems: FCHS System1 and FCHS System2. We evaluate the proposed systems by simulations and have shown that FCHS System2 make a good selection of the cluster head compared with FCHS System1 and another previous system.Peer ReviewedPostprint (published version

Target Tracking in Wireless Sensor Network

Wireless Sensor Networks (WSNs) consist of small nodes with sensing, computation, and wireless communications capabilities. In this paper target tracking using dynamic clustering technique has been presented. The dynamic clustering mechanism proposed performs the clustering along the route of the target movement with minimum numbers of sensor nodes to track the target object. The sensors detecting the object need to transmit the sensing data and identification. Sensors forming clusters are termed as core sensors. Within each cluster, the core sensors are selected based on the estimated signal strength since the nodes closer to the targets having larger measurements have a higher probability of becoming core sensors. The core sensors are used to compute the location of a target based on the locations of the neighbouring nodes. These core sensors send this information to the corresponding Cluster Head (CH), using which the target localization is processed. The position of moving object is detected by object moving algorithm. The location is sent to sink from CH node. Target tracking is used in traffic tracking and vehicle tracking. DOI: 10.17762/ijritcc2321-8169.16047

TRACKING OF MOVING OBJECT IN WIRELESS SENSOR NETWORK

A Wireless Sensor Network is a collection of sensor nodes distributed into a network to monitor the environmental conditions and send the sensed data to the Base Station. Wireless Sensor Network is one of the rapidly developing area in which energy consumption is the most important aspect to be considered while tracking, monitoring, reporting and visualization of data. An Energy Efficient Prediction-based Clustering algorithm is proposed to track the moving object in wireless sensor network. This algorithm reduces the number of hops between transmitter and receiver nodes and also the number of transmitted packets. In this method, the sensor nodes are statically placed and clustered using LEACH-R algorithm. The Prediction based clustering algorithm is applied where few nodes are selected for tracking which uses the prediction mechanism to predict the next location of the moving object. The Current Location of the target is found using Trilateration algorithm. The Current Location or Predicted Location is sent to active Cluster Head from the leader node or the other node. Based on which node send the message to the Cluster Head, the Predicted or Current Location will be sent to the base station. In real time, the proposed work is applicable in traffic tracking and vehicle tracking. The experiment is carried out using Network Stimulator-2 environment. Simulation result shows that the proposed algorithm gives a better performance and reduces the energy consumption

Implementation of CAVENET and its usage for performance evaluation of AODV, OLSR and DYMO protocols in vehicular networks

Vehicle Ad-hoc Network (VANET) is a kind of Mobile Ad-hoc Network (MANET) that establishes wireless connection between cars. In VANETs and MANETs, the topology of the network changes very often, therefore implementation of efficient routing protocols is very important problem. In MANETs, the Random Waypoint (RW) model is used as a simulation model for generating node mobility pattern. On the other hand, in VANETs, the mobility patterns of nodes is restricted along the roads, and is affected by the movement of neighbour nodes. In this paper, we present a simulation system for VANET called CAVENET (Cellular Automaton based VEhicular NETwork). In CAVENET, the mobility patterns of nodes are generated by an 1-dimensional cellular automata. We improved CAVENET and implemented some routing protocols. We investigated the performance of the implemented routing protocols by CAVENET. The simulation results have shown that DYMO protocol has better performance than AODV and OLSR protocols.Peer ReviewedPostprint (published version

An intelligent fuzzy-based cluster head selection system for WSNs and its performance evaluation for D3N parameter

Cluster formation and cluster head selection are important problems in sensor network applications and can drastically affect the network's communication energy dissipation. However, selecting of the cluster head is not easy in different environments which may have different characteristics. In our previous work, in order to deal with this problem, we proposed a power reduction algorithm for sensor networks based on fuzzy logic and number of neighbour nodes. We call this algorithm F3N. In this paper, we evaluate F3N and LEACH by some simulation results. From the simulation results, we found that the probability of a not to be a cluster head is increased with increase of number of neighbour nodes and remained battery power decrease of distance from the cluster centroid.Peer ReviewedPostprint (published version

An intelligent fuzzy-based cluster head selection system for wireless sensor networks and its performance evaluation

Sensor networks supported by recent technological advances in low power wireless communications along with silicon integration of various functionalities are emerging as a critically important computer class that enable novel and low cost applications. There are many fundamental problems that sensor networks research will have to address in order to ensure a reasonable degree of cost and system quality. Cluster formation and cluster head selection are important problems in sensor network applications and can drastically affect the network's communication energy dissipation. However, selecting of the cluster head is not easy in different environments which may have different characteristics. In our previous work, in order to deal with this problem, we proposed a power reduction algorithm for sensor networks based on fuzzy logic and number of neighbor nodes. We call this algorithm F3N. In this paper, we implement a simulation system for clustering algorithms in sensor networks. We evaluate LEACH and F3N by some simulation results. Presently, we have implemented LEACH algorithm in NS-2. However, F3N is implemented in MATLAB. We are working to implement also F3N system in NS-2 in order to compare their performance.Peer ReviewedPostprint (published version

Cluster Head Selection in a Homogeneous Wireless Sensor Network Ensuring Full Connectivity with Minimum Isolated Nodes

The research work proposes a cluster head selection algorithm for a wireless sensor network. A node can be a cluster head if it is connected to at least one unique neighbor node where the unique neighbor is the one that is not connected to any other node. If there is no connected unique node then the CH is selected on the basis of residual energy and the number of neighbor nodes. With the increase in number of clusters, the processing energy of the network increases; hence, this algorithm proposes minimum number of clusters which further leads to increased network lifetime. The major novel contribution of the proposed work is an algorithm that ensures a completely connected network with minimum number of isolated nodes. An isolated node will remain only if it is not within the transmission range of any other node. With the maximum connectivity, the coverage of the network is automatically maximized. The superiority of the proposed design is verified by simulation results done in MATLAB, where it clearly depicts that the total numbers of rounds before the network dies out are maximum compared to other existing protocols

On the performance of a GPU-based SoC in a distributed spatial audio system

[EN] Many current system-on-chip (SoC) devices are composed of low-power multicore processors combined with a small graphics accelerator (or GPU) offering a trade-off between computational capacity and low-power consumption. In this context, spatial audio methods such as wave field synthesis (WFS) can benefit from a distributed system composed of several SoCs that collaborate to tackle the high computational cost of rendering virtual sound sources. This paper aims at evaluating important aspects dealing with a distributed WFS implementation that runs over a network of Jetson Nano boards composed of embedded GPU-based SoCs: computational performance, energy efficiency, and synchronization issues. Our results show that the maximum efficiency is obtained when the WFS system operates the GPU frequency at 691.2 MHz, achieving 11 sources-per-Watt. Synchronization experiments using the NTP protocol show that the maximum initial delay of 10 ms between nodes does not prevent us from achieving high spatial sound quality.This work has been supported by the Spanish Government through TIN2017-82972-R, ESP2015-68245-C4-1-P, the Valencian Regional Government through PROMETEO/2019/109 and the Universitat Jaume I through UJI-B2019-36.Belloch, JA.; Badía, JM.; Larios, DF.; Personal, E.; Ferrer Contreras, M.; Fuster Criado, L.; Lupoiu, M.... (2021). On the performance of a GPU-based SoC in a distributed spatial audio system. The Journal of Supercomputing (Online). 77(7):6920-6935. https://doi.org/10.1007/s11227-020-03577-46920693577