Minimum Number Of Active Nodes Research Articles

Energy-efficient coverage optimization in wireless sensor networks based on Voronoi-Glowworm Swarm Optimization-K-means algorithm

Deployment of sensor nodes is one of the crucial factors in mobile wireless sensor networks for improving the performance of the network. The network's lifetime primarily depends on the consumed energy and area coverage by the sensor nodes. The efficiency of mobile wireless sensor networks increases by the efficient deployment of the sensors. Coverage and energy consumption mainly depends on the effective deployment schemes of sensors. This article presents an energy-efficient coverage optimization technique with the help of the Voronoi-Glowworm Swarm Optimization-K-means algorithm. In this approach, Glowworm Swarm Optimization, K-means algorithm, and Voronoi cell structure enhance the coverage area with a minimum number of active nodes. This approach considers optimum sensing radius calculation for efficient sensor deployment. Furthermore, the proposed method improves the lifetime of the deployed network by decreasing the consumed energy by the deployed sensor nodes using multi-hop transmission and the sleep-wake mechanism. The simulation result shows that area coverage is achieved by the proposed method up to 99.99% with the optimum number of active sensor nodes.

Exploiting Layered Multi-Path Routing Protocols to Avoid Void Hole Regions for Reliable Data Delivery and Efficient Energy Management for IoT-Enabled Underwater WSNs.

The key concerns to enhance the lifetime of IoT-enabled Underwater Wireless Sensor Networks (IoT-UWSNs) are energy-efficiency and reliable data delivery under constrained resource. Traditional transmission approaches increase the communication overhead, which results in congestion and affect the reliable data delivery. Currently, many routing protocols have been proposed for UWSNs to ensure reliable data delivery and to conserve the node’s battery with minimum communication overhead (by avoiding void holes in the network). In this paper, adaptive energy-efficient routing protocols are proposed to tackle the aforementioned problems using the Shortest Path First (SPF) with least number of active nodes strategy. These novel protocols have been developed by integrating the prominent features of Forward Layered Multi-path Power Control One (FLMPC-One) routing protocol, which uses 2-hop neighbor information, Forward Layered Multi-path Power Control Two (FLMPC-Two) routing protocol, which uses 3-hop neighbor information and ’Dijkstra’ algorithm (for shortest path selection). Different Packet Sizes (PSs) with different Data Rates (DRs) are also taken into consideration to check the dynamicity of the proposed protocols. The achieved outcomes clearly validate the proposed protocols, namely: Shortest Path First using 3-hop neighbors information (SPF-Three) and Breadth First Search with Shortest Path First using 3-hop neighbors information (BFS-SPF-Three). Simulation results show the effectiveness of the proposed protocols in terms of minimum Energy Consumption (EC) and Required Packet Error Rate (RPER) with a minimum number of active nodes at the cost of affordable delay.

DEHCIC: A distributed energy-aware hexagon based clustering algorithm to improve coverage in wireless sensor networks

Due to the random deployment of Wireless Sensor Network (WSN) nodes in dangerous and inaccessible environments and their sensing radius limitations, complete coverage of the monitoring area cannot be achieved. Clustering techniques, applying the mobile nodes, and adjusting the sensing radius of sensor nodes are regarded as practical techniques to improve the coverage and reduce the energy consumption. The challenges including, how to grouping the nodes, selecting the cluster heads, managing the mobility of mobile nodes, and scheduling sleep intervals should be considered in the hybrid techniques to improve the coverage with minimum overlapping. In this paper, we propose a DEHCIC (Distributed Energy-aware Hexagon based Clustering algorithm to Improve Coverage) algorithm that considers energy and topological features such as the number of mobile neighbor nodes and number of neighbor nodes for electing the cluster heads. In addition, DEHCIC attempts to cover the holes as much as possible by the static sensor nodes so that if it is not possible, the closest mobile will cover the coverage holes. Moreover, the DEHCIC algorithm retains the sensor nodes in the active mode that cover the interest points; also, it puts others into the low-powered sleep mode. The simulation results show that the proposed algorithm reduces dependency on the movement of mobile nodes and with the minimum number of active nodes can prolong the coverage lifetime and improve the network coverage effectively.

α-Overlapping area coverage for clustered directional sensor networks

Area coverage problem in Directional Sensor Networks (DSNs) presents great research challenges including minimization of number of active sensors and overlapping sensing coverage area among them, determination of their active sensing directions in an energy-efficient way, etc. Existing solutions permit to execute coverage enhancement algorithms at each individual sensor nodes, leading to high communication and computation overheads, loss of energy and reduced sensing coverage. In this paper, we first formulate the problem of maximizing area coverage with minimum number of active nodes as a mixed-integer linear programming (MILP) optimization problem for a clustered DSN. Due to its NP-completeness, we then develop a greedy alternate solution, namely α-overlapping area coverage (α-OAC). In α-OAC, each cluster head (CH) takes the responsibility of determining the active member nodes and their sensing directions, where, each sensing node is allowed to have at most α% coverage overlapping with its neighbors. The α-OAC CHs activate a sensor node iif the later has sufficient residual energy and send other member nodes to the sleep state. The proposed α-OAC system is distributed and scalable since it requires single-hop neighborhood information only. Results from extensive simulations, done in NS-3, reveal that the α-OAC system outperforms state-of-the-art works in terms of area coverage, network lifetime and operation overhead.

Connectivity, coverage and power consumption in large-scale wireless sensor networks

Wireless sensor networks (WSNs) are typically constituted by a large number of connected sensors (nodes), generally distributed at random on a given surface area. In such large-scale networks, the desired global system performance is achieved by gathering local information and decisions collected from each individual node. There exist three fundamental global issues on WSNs that we consider here, namely, full network connectivity, high coverage of the sensing area and reduced power consumption, thus improving on the network lifetime. Full connectivity can be obtained either by increasing the transmission range, at the expense of consuming higher transmission power, or by increasing the number of sensors, i.e. by increasing network costs. Both of them are closely related to global network lifetime, in the sense that the higher the power consumption or the more the number of active sensors present, the shorter the network lifetime (Wang et al., 2007) [1]. Here, we are interested in the minimal number of active nodes required for keeping the network functioning, while the problem of redundancy, i.e. having additional nodes kept in a sleeping mode for a certain period of time, can be implemented afterwards based on the present ‘minimal’ results. So the main question is, how can one design large-scale random networks in order to have both global connectivity and minimum number of active nodes reducing the total energy consumption? Although these questions have been addressed often in the past, a definite, simple predicting algorithm for achieving these goals does not exist so far. In this paper, we aim to discuss such a scheme and confront it with extensive simulations of random networks generated numerically. Specifically, we study the minimum number of nodes required to achieve full network connectivity, and present an analytical formula for estimating it. The results are in very good agreement with the numerical simulations as a function of transmission range. We also discuss results on how to further diminish network energy consumption by switching off some of the active nodes at random by keeping the connectivity of the whole network. The present results are expected to be useful for the design of more efficient WSNs.

An Adaptive Scheduling Algorithm for Set Cover Problem in Wireless Sensor Networks: A Cellular Learning Automata Approach

Redundant node deployment is a common strategy in wireless sensor networks. This redundancy can be due to various reasons such as high probability of failures, long lifetime expectation, etc. One major problem in wireless sensor networks is to use this redundancy in order to extend the network lifetime while keeping the entire area under the coverage of the network. In this problem, which is known as set cover problem, the main objective is to select a subset of sensor nodes as active nodes so that the set of active nodes covers the entire area of the network. In this paper, an scheduling algorithm is presented for solving the set cover problem using cellular learning automata. In this algorithm, each node is equipped with a learning automaton which locally decides for the node to be active or not based on the situations of its neighbors. Simulation results in J-sim simulator environment specify the efficiency of the proposed scheduling algorithm over existing algorithms such as PEAS and PECAS. In this paper, a distributed, adaptive scheduling algorithm based on cellular learning automata for wireless sensor networks is proposed. The main purpose of this algorithm is to maintain coverage in the network with minimum number of active nodes, so that the total consumed energy of nodes is minimized. In the proposed algorithm, each node is equipped with a learning automaton which decides for the node to be active or not based on the states (either active or not) of its neighboring sensor nodes. We used J-sim simulator to evaluate the performance of the proposed algorithm. Simulation results specify the efficiency of the proposed algorithm over existing algorithms such as PEAS (2) and PECAS (3) -especially against high ratio of unexpected failures. The rest of this paper is organized as follows. In Section II, a literature overview is presented. In Section III, learning automata and cellular learning automata are briefly reviewed. The problem statement is given in Section IV. The proposed scheduling algorithm is described in Section V. Simulation results are given in Section VI. Section VII is the conclusion.

A Dead-End Free Topology Maintenance Protocol for Geographic Forwarding in Wireless Sensor Networks

Minimizing energy consumption is a fundamental requirement when deploying wireless sensor networks. Accordingly, various topology control protocols have been proposed, which aim to conserve energy by turning off unnecessary sensors while simultaneously preserving a constant level of routing fidelity. However, although these protocols can generally be integrated with any routing scheme, few of them take specific account of the issues which arise when they are integrated with geographic routing mechanisms. Of these issues, the dead-end situation is a particular concern. The dead-end phenomenon (also known as the "local maximum” problem) poses major difficulties when performing geographic forwarding in wireless sensor networks since whenever a packet encounters a dead end, additional overheads must be paid to forward the packet to the destination via an alternative route. This paper presents a distributed dead-end free topology maintenance protocol, designated as DFTM, for the construction of dead-end free networks using a minimum number of active nodes. The performance of DFTM is compared with that of the conventional topology maintenance schemes GAF and Span, in a series of numerical simulations conducted using the ns2 simulator. The evaluation results reveal that DFTM significantly reduced the number of active nodes required in the network and thus prolonged the overall network lifetime. DFTM also successfully constructed a dead-end free topology in most of the simulated scenarios. Additionally, even when the locations of the sensors were not precisely known, DFTM still ensured that no more than a very few dead-end events occurred during packet forwarding.

Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius

In this paper, the problem of maintaining sensing coverage by keeping a small number of active sensor nodes and a small amount of energy consumption in a wireless sensor network is studied. As opposed to the uniform sensing model previously, we consider a large number of sensors with adjustable sensing radius that are randomly deployed to monitor a target area. A novel coverage control scheme based on elitist non-dominated sorting genetic algorithm (NSGA-II) is proposed in a heterogeneous sensor network. By devising a cluster-based architecture, the algorithm is applied in a distributed way. Furthermore, an ameliorated binary coding is addressed to represent both sensing radius adjustment and sensor selection. Numerical and simulation results validate that the procedure to find the optimal balance point among the maximum coverage rate, the least energy consumption, as well as the minimum number of active nodes is fast and effective.