Minimum Number Of Relay Nodes Research Articles

Node placement optimization using particle swarm optimization and iterated local search algorithm in wireless sensor networks

SummaryCoverage and connectivity are the two most challenging issues in target‐based wireless sensor networks (WSNs). For that, node placement is one of the fundamental concerns that affect the performance of coverage and connectivity in WSN. This paper introduces a new approach by combining particle swarm optimization and iterated local search (PSO‐ILS) to have an optimum coverage and connectivity rate with the minimum number of nodes. In one side, to maintain the full coverage of targets, the PSO‐ILS is used to deploy the minimum number of sensor nodes. In other side, to achieve the full connectivity, the optimal position determination (OPD) algorithm was conceived to identify the optimal candidate positions which can be used by the PSO‐ILS to place the minimum number of relay nodes. The obtained results considered over a number of runs are compared with canonical PSO, differential evolution (DE), and genetic algorithms (GAs). The outcomes derived from this comprehensive analysis determine that PSO‐ILS provides an effectual improvement in contrary to the methods PSO, DE, and GA in terms of the selected potential positions to ensure full coverage of target points and the number of relay nodes required to achieve full connectivity.

Optimized Node Deployment in Wireless Sensor Network for Smart Grid Application

At present the low cost, low power and collaborative feature of Wireless Sensor Network (WSN) is becoming a popular communication technology in smart grid including power generation, transmission and distribution. Among these, the health monitoring of wind power generation system has emerged as one of the many possible applications of WSNs. However the harsh environmental condition of wind farm application brings node deployment as a major design issue in WSN which is well associated with coverage and connectivity issues. Hence the research objective here is twofold. Firstly the sensor nodes are placed optimally in the key components of the wind turbines to improve target coverage. The adjacent turbine span varies with several hundred meters apart which results in independent wireless sensor sub-networks. Connectivity among these sub-networks is a second vital issue, which is guaranteed by joining all the independent sub-networks with the base station by placing minimum number of relay nodes. Hence the connectivity problem is considered as Relay Node Deployment Problem. Connectivity is obtained in this work by bio-inspired Ant Colony Optimization (ACO) algorithm. ACO is further enhanced as ACO-Intelligent Movement, by introducing intelligent movement mechanism. The goal of this approach is to optimize number of relay nodes, decrease deployment cost and to bring up network connectivity. The performance of our novel deployment approach is validated through extensive simulation results.

Energy Efficient Model for Recovery from Multiple Nodes Failure in Wireless Sensor Networks

Connectivity among deployed sensor nodes in wireless sensor network (WSN) is essential for collection and transmission of sensed data. In harsh environmental conditions, it is difficult to maintain the network connectivity in case of failures of participating nodes, that results disjoint partitions of the network. In this paper, an attempt has been made for WSNs, to mitigate both connectivity and coverage loss for partitioned network in energy-efficient manner. In the proposed model, relay nodes have been used as anchor node for the deployed sensor nodes. A heuristic is proposed for relay nodes placement in this work, because finding minimum number of relay nodes required covering a network is known NP-hard problem. The proposed model is divided into two phases: intra-partition phase and inter-partition phase. In the intra-partition phase, the redundant nodes spread towards the boundary of the partitions and spreading gradually increases the coverage of each partition. The nodes are moved in iterations up to the representative area of relay nodes. In the inter-partition phase, a game theoretic approach is proposed to restore connectivity between partitions obtained from intra-partition phase. The proposed model is experimentally analyzed and evaluated through simulations and outperforms when compared with existing algorithms.

RB-IEMRP: RELAY BASED IMPROVED THROUGHPUT ENERGY-EFFICIENT MULTI-HOP ROUTING PROTOCOL FOR INTRA BODY SENSOR NETWORK (INTRA-WBSN)

In this paper, we have proposed a Relay based Improved Throughput and Energy-efficient Multi-hop Routing Protocol (Rb-IEMRP) for the Intra Wireless Body Sensor Network (Intra-WBSN). Moreover, mathematical analysis has been presented, to calculate the minimum number of relay nodes require to be deployed corresponding to the bio-sensor nodes in Intra-WBSN. Normal sensing data from bio-sensor nodes forwarded to BNC through relay nodes while emergency data is directly transmitted to BNC. Relays nodes are placed in the patients' cloth. It can be easily replaced or recharged that facilitates effective health monitoring. The proposed routing protocol has achieved better network stability, network lifetime, energy efficiency and throughput as compared to Stable Increased Throughput Multi-Hop Protocol for Link Efficiency in Wireless Body Area Networks (SIMPLE) and Reliable Energy Efficient Critical Data Routing in Wireless Body Area Networks (REEC) routing protocols. It has been validated through simulation results.

Relay node deployment for wireless sensor networks using evolutionary multi-objective algorithm

In this paper, we study the relay node placement problem of, given a deployment of sensor nodes, finding the minimum number of relay nodes to increase the lifetime and reduce the deployment costs of wireless sensor networks (WSNs) simultaneously. In particular, we use the average remaining energy of the relay nodes to express the lifetimes of the WSNs. Due to the conflict of the average remaining time and deployment costs of relay nodes, we first formulate the relay node placement problem as a multi-objective optimisation problem, which is difficult to address using traditional methods. Based on this, a novel evolutionary multi-objective algorithm is presented to solve the problem. Furthermore, a local search method is presented to improve the convergence of the algorithm. Simulation results show that the proposed relay node placement scheme can efficiently reduce the cost and increase the lifetime.

ISSUES OF CONSTRUCTION OF TELECOMMUNICATION SYSTEM OF COMMUNICATION AND TRANSFER OF DATA OF SELF-PROPELLED ARTILLERY DIVISION OF AIRBORNE FORCES

The paper considers the list of functions to be implemented in the special software of the artillery control automation control system of the self-propelled artillery division of the airborne forces to ensure the reliability of operation. The principles implemented in the control subsystem for the control of the operation of the artillery control automation control system developed by the Rubin research and production enterprise are described. They make it possible to significantly reduce the load on network and computing resources by eliminating parallel streams of requests for duplicate data from simultaneously performed special software tasks. Solutions are proposed for automating the operation of the communication and data transmission subsystem, providing reliable data transmission, automatic selection of the communication channel having the highest throughput, automatic retransmission route construction, providing the maximum capacity with the minimum number of relay nodes during the movement of control machines and self-propelled artillery pieces. The analysis of the transport level protocols is carried out and the choice of the optimal protocol - SCTP is proved. The analysis of routing protocols is made and the choice of the optimal protocol - AODV is justified.

Relay node placement under budget constraint

The relay node placement problem in the wireless sensor network domain has been studied extensively. But under a fixed budget, it may be impossible to procure the minimum number of relay nodes needed to design a connected network of sensor and relay nodes. Nevertheless, one would still like to design a network with high level of connectedness, or low disconnectedness. In this paper, we introduce the notion of a measure of the “connectedness” of a disconnected graph. We study a family of problems whose goal is to design a network with “maximal connectedness” subject to a fixed budget constraint.

Optimization of relay node deployment for multisource multipath routing in Wireless Multimedia Sensor Networks using Gaussian distribution

In Wireless Multimedia Sensor Networks, source nodes send intense multimedia traffic, under strict quality-of-service requirements, to the base station via resource-limited relay nodes. To satisfy such requirements, all discoverable routing paths must be concurrently utilized. The shorter the paths are, the lower the energy consumption and the shorter the packet delay are, resulting in an enhanced network lifespan and higher rates of data delivery.Optimizing the number and the deployment strategy of relay nodes has been a vital problem to solve. Focusing mainly on single source node scenarios, previous theoretical studies assumed that either a deterministic or a random uniform distribution of relay nodes is to be used. Recent studies, however, have proven that when practical deployment strategies are used, controllable Gaussian distributions of relay nodes can be achieved, and both network resources and routing performance can otherwise be optimized.This paper presents an analytical framework for the optimal deployment of relay nodes using a two-dimensional Gaussian distribution. Given the network parameters (such as number of sources nodes, communication range and number of routing paths needed) both the theoretical minimum number of relay nodes needed and the optimal standard deviation can be exactly determined. To validate the mathematical model and evaluate its performance, a simulation study has been conducted. Statistical analysis revealed that when optimal Gaussian distribution was used, instead of a uniform distribution, routing performance increased by at least twofold. That is sufficiently close to the ideal deterministic distribution. Additionally, the average path length and the number of control packets were considerably reduced, which can potentially improve energy consumption, packet delay, network lifetime and data delivery.

P.1.g.046 - New insights on the binding profile of loxapine on dopaminergic and serotonergic receptors

In Wireless Multimedia Sensor Networks, source nodes send intense multimedia traffic, under strict quality-of-service requirements, to the base station via resource-limited relay nodes. To satisfy such requirements, all discoverable routing paths must be concurrently utilized. The shorter the paths are, the lower the energy consumption and the shorter the packet delay are, resulting in an enhanced network lifespan and higher rates of data delivery.Optimizing the number and the deployment strategy of relay nodes has been a vital problem to solve. Focusing mainly on single source node scenarios, previous theoretical studies assumed that either a deterministic or a random uniform distribution of relay nodes is to be used. Recent studies, however, have proven that when practical deployment strategies are used, controllable Gaussian distributions of relay nodes can be achieved, and both network resources and routing performance can otherwise be optimized.This paper presents an analytical framework for the optimal deployment of relay nodes using a two-dimensional Gaussian distribution. Given the network parameters (such as number of sources nodes, communication range and number of routing paths needed) both the theoretical minimum number of relay nodes needed and the optimal standard deviation can be exactly determined. To validate the mathematical model and evaluate its performance, a simulation study has been conducted. Statistical analysis revealed that when optimal Gaussian distribution was used, instead of a uniform distribution, routing performance increased by at least twofold. That is sufficiently close to the ideal deterministic distribution. Additionally, the average path length and the number of control packets were considerably reduced, which can potentially improve energy consumption, packet delay, network lifetime and data delivery.

Energy-Aware Constrained Relay Node Deployment for Sustainable Wireless Sensor Networks

This paper considers the problem of communication coverage for sustainable data forwarding in wireless sensor networks, where an energy-aware deployment model of relay nodes (RNs) is proposed. The model used in this paper considers constrained placement and is different from the existing one-tiered and two-tiered models. It supposes two different types of sensor nodes to be deployed, i) energy rich nodes (ERNs), and ii) energy limited nodes (ELNs). The aim is thus to use only the ERNs for relaying packets, while ELN's use will be limited to sensing and transmitting their own readings. A minimum number of RNs is added if necessary to help ELNs. This intuitively ensures sustainable coverage and prolongs the network lifetime. The problem is reduced to the traditional problem of minimum weighted connected dominating set (MWCDS) in a vertex weighted graph. It is then solved by taking advantage of the simple form of the weight function, both when deriving exact and approximate solutions. Optimal solution is derived using integer linear programming (ILP), and a heuristic is given for the approximate solution. Upper bounds for the approximation of the heuristic (versus the optimal solution) and for its runtime are formally derived. The proposed model and solutions are also evaluated by simulation. The proposed model is compared with the one-tiered and two-tiered models when using similar solution to determine RNs positions, i.e., minimum connected dominating set (MCDS) calculation. Results demonstrate the proposed model considerably improves the network life time compared to the one-tiered model, and this by adding a lower number of RNs compared to the two-tiered model. Further, both the heuristic and the ILP for the MWCDS are evaluated and compared with a state-of-the-art algorithm. The results show the proposed heuristic has runtime close to the ILP while clearly reducing the runtime compared to both ILP and existing heuristics. The results also demonstrate scalability of the proposed solution.

Energy-sustainable relay node deployment in wireless sensor networks

Emergence of diverse renewable energy harvesting technologies and their incorporation into tiny sensor devices have given birth to Energy Harvesting Wireless Sensor Networks (EH-WSNs), where the problem domain has shifted from energy conservation to energy sustainability of the network. Renewable energy harvesting and depletion of sensor devices are stochastic and thus, energy availability in the devices is sporadic rather than continuous. Therefore, the optimal deployment of data routing devices (i.e., relay nodes) and their activity scheduling to ensure that, the data from all source sensors could be routed to the sink while keeping the network functional perpetually, is a challenging research problem. In this paper, we develop a multi-constraint mixed integer linear program (MILP) to minimize the number of relay nodes to be deployed in the network, while considering connectivity, sustainability and unpredictable energy harvesting and depletion rates. We refer to this problem as SMRMC (sustainable minimum-relay maximum-connectivity deployment) which is proved to be NP-hard. A light weight k-connected greedy solution to the SMRMC problem has been developed first for k=1, and thereafter, a generalized solution has been presented for any k (k ≥ 2) by constructing convex-polytopes among the existing relay nodes. Extensive simulation experiments have been conducted to validate the performance of the proposed deployment strategies. Performance studies carried out in MATLAB, show that the proposed SMRMC algorithms can achieve up to twice the network lifetime compared to state-of-the-art approaches whilst deploying minimum number of relay nodes.

Fault Prediction and Relay Node Placement inWireless Sensor Network

In Wireless Sensor Network (WSN), collected data will be faulty due to internal and external influences, such as environmental conditions, communication link failure, battery drain, etc. These are reduces the reliability of the WSN network. Faults may affect on quality of services (QoS), in WSN networks faults may produce incorrect data provided by sensor nodes or the network may make a misjudgment on nodes or the network and placing relay nodes to tolerate these faults, to improve Qos and reliability of the network. In this work, we propose the prediction model Support Vector Machine (SVM) to predict faulty nodes in wireless sensor network and add minimum number of relay nodes as compared to existing system to recover the network proactively from the faults and to continue working successfully.

Restoration Strategy Based on Optimal Relay Node Placement in Wireless Sensor Networks

In wireless sensor networks (WSNs), connecting disjoint segments is significant for network restoration, especially in some mission critical applications. However, the variability of distances between disjoint segments has tremendous influence on relay nodes deployment. In fact, finding the optimal solution for connecting disjoint segments in terms of the number and positions of relay nodes is NP-hard. To address this issue, plenty of heuristics, such as STP-MSP (Cheng et al., 2008), MST-1 tRN (Lloyd et al., 2007), and CORP (Lee and Younis, 2010) are deeply pursued. In this paper, we propose a distributed restoration algorithm based on optimal relay node placement (simply, ORNP). It aims at federating separated segments by populating the minimum number of relay nodes in a WSN that has suffered a significant damage. In addition, both of complexity and upper bound of the relay count for ORNP are explored. The simulation results show that ORNP performs better than STP-MSP, MST-1 tRN, and CORP in terms of relay count and the connectivity of resulting topology.

Linear Time Approximation Algorithms for the Relay Node Placement Problem in Wireless Sensor Networks with Hexagon Tessellation

The relay node placement problem in wireless sensor network (WSN) aims at deploying the minimum number of relay nodes over the network so that each sensor can communicate with at least one relay node. When the deployed relay nodes are homogeneous and their communication ranges are circular, one way to solve the WSN relay node placement problem is to solve the minimum geometric disk cover (MGDC) problem first and place the relay nodes at the centers of the covering disks and then, if necessary, deploy additional relay nodes to meet the connection requirement of relay nodes. It is known that the MGDC problem is NP-complete. A novel linear time approximation algorithm for the MGDC problem is proposed, which identifies covering disks using the regular hexagon tessellation of the plane with bounded area. The approximation ratio of the proposed algorithm is (5+ϵ), where0<ϵ≤15. Experimental results show that the worst case is rare, and on average the proposed algorithm uses less than 1.7 times the optimal disks of the MGDC problem. In cases where quick deployment is necessary, this study provides a fast 7-approximation algorithm which uses on average less than twice the optimal number of relay nodes in the simulation.

Approximation Algorithms for Constrained Relay Node Placement in Energy Harvesting Wireless Sensor Networks

The constrained relay node placement problem in a wireless sensor network seeks the deployment of a minimum number of relay nodes (RNs) in a set of candidate locations in the network to satisfy specific requirements, such as connectivity or survivability. In this paper, we study the constrained relay node placement problem in an energy-harvesting network in which the energy harvesting potential of the candidate locations are known a priori. Our aim is to place a minimum number of relay nodes, to achieve connectivity or survivability, while ensuring that the relay nodes harvest large amounts of ambient energy. We present the connectivity and survivability problems, discuss their NP-hardness, and propose polynomial time ${\mbi{\cal O}}$ (1)-approximation algorithms with low approximation ratios to solve them. We validate the effectiveness of our algorithms through numerical results to show that the RNs placed by our algorithms harvest 50% more energy on average than those placed by the algorithms unaware of energy harvesting. We also develop a unified-mixed integer linear program (MILP)-based formulation to compute a lower bound of the optimal solution for minimum relay node placement and demonstrate that the results of our proposed algorithms were on average within 1.5 times of the optimal.

A shortest path tree based algorithm for relay placement in a wireless sensor network and its performance analysis

In this paper, we study a problem of designing a multi-hop wireless network for interconnecting sensors (hereafter called source nodes) to a Base Station (BS), by deploying a minimum number of relay nodes at a subset of given potential locations, while meeting a quality of service (QoS) objective specified as a hop count bound for paths from the sources to the BS. The hop count bound suffices to ensure a certain probability of the data being delivered to the BS within a given maximum delay under a light traffic model. We observe that the problem is NP-Hard. For this problem, we propose a polynomial time approximation algorithm based on iteratively constructing shortest path trees and heuristically pruning away the relay nodes used until the hop count bound is violated. Results show that the algorithm performs efficiently in various randomly generated network scenarios; in over 90% of the tested scenarios, it gave solutions that were either optimal or were worse than optimal by just one relay. We then use random graph techniques to obtain, under a certain stochastic setting, an upper bound on the average case approximation ratio of a class of algorithms (including the proposed algorithm) for this problem as a function of the number of source nodes, and the hop count bound. To the best of our knowledge, the average case analysis is the first of its kind in the relay placement literature. Since the design is based on a light traffic model, we also provide simulation results (using models for the IEEE 802.15.4 physical layer and medium access control) to assess the traffic levels up to which the QoS objectives continue to be met.

Optimization of Wireless Sensor Network Lifetime by Deploying Relay Sensors

Topology control in wireless sensor networks helps to lower node energy consumption by reducing transmission power and by confining interference, collisions and consequently retransmissions. Decrease in node energy consumption implies probability of increasing network lifetime. In this paper, firs popular topology control algorithms are used for analyzing optimizing the power consumption in the wireless sensor network and later proposed a novel technique wherein power consumption is traded with additional relay nodes. Later relay nodes are introduced to make the network connected without increasing the transmit power. The relay node decreases the transmit power required while it may increase end-to-end delay. This paper designs and analyzes an algorithm that place an almost minimum number of relay nodes required to make network connected. Greedy version of this algorithm is implemented and demonstrated in simulation that it produces a high quality link. InterAvg, InterMax (no of nodes that can offer interference) MinMax, and MinTotal are used as metrics to analyze and compare various algorithms. Matlab and NS-2 are used for simulation purpose.

Relay node placement in two-tiered wireless sensor networks with base stations

This paper addresses the relay node placement problem in two-tiered wireless sensor networks with base stations, which aims to deploy a minimum number of relay nodes to achieve certain coverage and connectivity requirement. Under the assumption that the communication range of the sensor nodes is no more than that of the relay node, we present a polynomial time (5+∈)-approximation algorithm for the 1-coverage 1-connected problem. Furthermore, we consider the fault tolerant problem in the network, we present a polynomial time (20+∈)-approximation algorithm for the 2-coverage 2-connected problem, where ∈ is any given positive constant. For the k-coverage 2-connected situation, we present a polynomial time (15k?10+∈)-approximation algorithm.

Relay node placement based on balancing power consumption in wireless sensor networks

To alleviate the traffic burden of sensor nodes and extend the network lifetime in wireless sensor networks, one approach is to deploy a large number of low-cost micro-relay nodes for routing data. The question is how to deploy a minimum number of relay nodes in the sensing field with network lifetime constraint. If relay nodes are uniformly placed, the issue of uneven power consumption arises as relay nodes closer to the sink are required to relay more data and thus will deplete their energy more quickly. Here, the authors study the relay node deployment problem under the assumption that the relay node has the same energy supply as the sensor node. Based on balancing power consumption among all sensor nodes and relay nodes, the authors deduce a relay node density function according to which relay nodes are placed in the sensing field. Simulation results show that the approach can achieve high energy utilisation and the network lifetime is significantly extended compared with uniformly placing relay nodes.

Fault-Tolerant Relay Node Placement in Heterogeneous Wireless Sensor Networks

Existing work on placing additional relay nodes in wireless sensor networks to improve network connectivity typically assumes homogeneous wireless sensor nodes with an identical transmission radius. In contrast, this paper addresses the problem of deploying relay nodes to provide fault tolerance with higher network connectivity in heterogeneous wireless sensor networks, where sensor nodes possess different transmission radii. Depending on the level of desired fault tolerance, such problems can be categorized as: 1) full fault-tolerant relay node placement, which aims to deploy a minimum number of relay nodes to establish k(k ? 1) vertexdisjoint paths between every pair of sensor and/or relay nodes and 2) partial fault-tolerant relay node placement, which aims to deploy a minimum number of relay nodes to establish k(k ? 1) vertex-disjoint paths only between every pair of sensor nodes. Due to the different transmission radii of sensor nodes, these problems are further complicated by the existence of two different kinds of communication paths in heterogeneous wireless sensor networks, namely, two-way paths, along which wireless communications exist in both directions; and one-way paths, along which wireless communications exist in only one direction. Assuming that sensor nodes have different transmission radii, while relay nodes use the same transmission radius, this paper comprehensively analyzes the range of problems introduced by the different levels of fault tolerance (full or partial) coupled with the different types of path (one-way or two-way). Since each of these problems is NP-hard, we develop O(?k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )-approximation algorithms for both one-way and two-way partial fault-tolerant relay node placement, as well as O(?k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> )-approximation algorithms for both one-way and two-way full fault-tolerant relay node placement (? is the best performance ratio of existing approximation algorithms for finding a minimum k-vertex connected spanning graph). To facilitate the applications in higher dimensions, we also extend these algorithms and derive their performance ratios in d-dimensional heterogeneous wireless sensor networks (d ? 3). Finally, heuristic implementations of these algorithms are evaluated via QualNet simulations.