Genetic algorithm with a new fitness function to enhance WSN lifetime
01 Oct 2015-pp 95-100
TL;DR: A methodology is proposed based on genetic algorithm with a new fitness function and a new operator called reconfiguration operator, which utilizes less number of sensors to define the set K-cover for scheduling the sensors.
Abstract: Deploying and maintaining wireless sensor networks (WSN) in remote places like volcano eruption, battle field, nuclear reactors and dense forest areas is pretty difficult or even sometimes impossible. Therefore there is a need to make a WSN which can function for long duration of time. Lifetime enhancement of WSN is a critical issue to be addressed before its being deployed in such remote areas. Since battery implanted on sensors has got very less power, efficient usage of the power can enhance the life of WSN. One of the solutions is designing proper scheduling of sensors using SET-K covers. Dividing the total sensors into N-groups where each group meets the constraint of full cover can enhance life of WSN by N times. However, dividing the total sensors into maximum number of N groups is called as K-cover problem which considered as NP-complete. In this paper, a methodology is proposed based on genetic algorithm with a new fitness function and a new operator called reconfiguration operator, which utilizes less number of sensors to define the set K-cover for scheduling the sensors.
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TL;DR: A hybrid approach based on combining particle swarm optimization (PSO) with random transition moves has been proposed to address the lifetime of a wireless sensor network with redundant deployment.
Abstract: The lifetime of a wireless sensor network (WSN) is a critical aspect as, in most of the applications, it is not possible to replace or recharge the batteries of the sensor nodes. The lifetime of a WSN with redundant deployment can be significantly increased by dividing the sensors into disjoint sets such that each of these sets, when operated independently, provides complete coverage of the targets. In order to maximize the lifetime of the network, the maximum possible number of such sets needs to be created. The problem has been proved to be nondeterministic polynomial complete. In this paper, a hybrid approach based on combining particle swarm optimization (PSO) with random transition moves has been proposed to address this problem. A swarm of randomly initialized particles explores the entire solution space in search of an optimum solution. Three novel random transition moves have been designed to exploit the redundancy in deployment of sensors and used to guide the randomly scattered particles towards the potential optimum solutions in their neighborhood. The transition moves escalate the convergence of the algorithm. The proposed algorithm has been tested both for point coverage and area coverage applications. To authenticate and validate the results, the comparison of the results is performed with the latest existing techniques. The proposed algorithm always finds the optimum solution by making fewer fitness function evaluations. The sensitivity analysis of the control parameters has also been performed.
10 citations
TL;DR: It is conjecture that similar traffic perturbation to altering the routing protocol can be achieved at the link layer through assignment of time slots to nodes.
9 citations
TL;DR: Three kinds of simple pure distributed algorithms named greedy game algorithms (GGAs) based on game theory for solving the SET-Cover problem are introduced and extensive experiments have been conducted to show the superiority in coverage, convergence, robustness, and network design of the proposed GGAs comparing with other existing algorithms.
Abstract: Coverage is a fundamental problem in heterogeneous wireless sensor networks (HWSNs). Lifetime of the HWSNs is another important problem in this area. The $K$ -Cover problem can solve both the coverage and lifetime issues. Especially, the lifetime of HWSNs can be prolonged by solving the SET $K$ -Cover problem through partitioning the sensors into $K$ sets. However, the SET $K$ -Cover problem has not been widely researched in HWSNs. Hence, we introduce three kinds of simple pure distributed algorithms named greedy game algorithms (GGAs) based on game theory for solving the SET $K$ -Cover problem in this paper, in which we consider the SET $K$ -Cover problem as a multi-stage game process. The sensors in HWSNs are considered as the players, the cover sets chosen by $N$ sensors as strategies, and the sensing area alone as the payoff function for each sensor. During the game process, each sensor wants to selfishly choose one of the $K$ -covers at each iteration, with which it can maximize its payoff value. After the game process, the best strategies all the players have chosen constitute the Nash Equilibrium. Finally, extensive experiments have been conducted to show the superiority in coverage, convergence, robustness, and network design of the proposed GGAs comparing with other existing algorithms.
5 citations
Cites background from "Genetic algorithm with a new fitnes..."
...Finally, the lifetime of the sensor networks can be extended to K times while the average coverage ratio also can be maximized at the same time [17]–[19], [21]....
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11 Jan 2018
TL;DR: The drawbacks of fitness functions used in the literature will be investigated and some solutions will be suggested, and the simulation results clearly show the improvement due to suggested solutions.
Abstract: Recent advances in wireless sensor network (WSN) technology are enabling the deployment of large-scale and collaborative sensor networks. WSNs face several challenges such as security, localisation, and energy consumption. To resolve these issues, evolutionary algorithms can be helpful. The core of every evolutionary algorithm is its fitness function. The drawbacks of fitness functions used in the literature will be investigated and some solutions will be suggested. The simulation results clearly show the improvement due to suggested solutions.
2 citations
01 Jan 2017
TL;DR: A novel concept, called WSNaaP (WSN as a platform), and a technical solution for interconnecting heterogeneous non-smart medical devices using an existing WSN infrastructure are introduced.
Abstract: Smart hospitals enhanced through the Internet and related technologies with live monitoring and interconnected devices, using wired and wireless sensors, provide better health services for patients However, legacy (non-smart) medical devices that are not able to communicate and to be managed by a smart hospital infrastructure are still in use Patients’ data can be collected from a wide range of non-smart medical device The paper introduces a novel concept, called WSNaaP (WSN as a platform) and a technical solution for interconnecting heterogeneous non-smart medical devices using an existing WSN infrastructure
2 citations
References
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TL;DR: The concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics is described.
17,936 citations
"Genetic algorithm with a new fitnes..." refers background in this paper
...Sensor activity scheduling is addressed in [8], [9], structure of network is addressed in [10], aggregation of data is addressed in [1], [11], data compression in [12] and routing protocols in [13]–[15]....
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04 Jan 2000
TL;DR: The Low-Energy Adaptive Clustering Hierarchy (LEACH) as mentioned in this paper is a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network.
Abstract: Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.
12,497 citations
01 Jan 2000
TL;DR: LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network, is proposed.
Abstract: Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have signicant impact on the overall energy dissipation of these networks. Based on our ndings that the conventional protocols of direct transmission, minimum-transmission-energy, multihop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster base stations (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show that LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional routing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.
11,412 citations
"Genetic algorithm with a new fitnes..." refers background in this paper
...Sensor activity scheduling is addressed in [8], [9], structure of network is addressed in [10], aggregation of data is addressed in [1], [11], data compression in [12] and routing protocols in [13]–[15]....
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02 Jul 2002
TL;DR: This paper model data-centric routing and compare its performance with traditional end-to-end routing schemes, and examines the complexity of optimal data aggregation, showing that although it is an NP-hard problem in general, there exist useful polynomial-time special cases.
Abstract: Sensor networks are distributed event-based systems that differ from traditional communication networks in several ways: sensor networks have severe energy constraints, redundant low-rate data, and many-to-one flows. Data-centric mechanisms that perform in-network aggregation of data are needed in this setting for energy-efficient information flow. In this paper we model data-centric routing and compare its performance with traditional end-to-end routing schemes. We examine the impact of source-destination placement and communication network density on the energy costs and delay associated with data aggregation. We show that data-centric routing offers significant performance gains across a wide range of operational scenarios. We also examine the complexity of optimal data aggregation, showing that although it is an NP-hard problem in general, there exist useful polynomial-time special cases.
1,536 citations
"Genetic algorithm with a new fitnes..." refers background in this paper
...Sensor activity scheduling is addressed in [8], [9], structure of network is addressed in [10], aggregation of data is addressed in [1], [11], data compression in [12] and routing protocols in [13]–[15]....
[...]
11 Jun 2001
TL;DR: The experimental results demonstrate that by using only a subset of sensor nodes at each moment, the system achieves a significant energy savings while fully preserving coverage.
Abstract: Wireless sensor networks have emerged recently as an effective way of monitoring remote or inhospitable physical environments. One of the major challenges in devising such networks lies in the constrained energy and computational resources available to sensor nodes. These constraints must be taken into account at all levels of the system hierarchy. The deployment of sensor nodes is the first step in establishing a sensor network. Since sensor networks contain a large number of sensor nodes, the nodes must be deployed in clusters, where the location of each particular node cannot be fully guaranteed a priori. Therefore, the number of nodes that must be deployed in order to completely cover the whole monitored area is often higher than if a deterministic procedure were used. In networks with stochastically placed nodes, activating only the necessary number of sensor nodes at any particular moment can save energy. We introduce a heuristic that selects mutually exclusive sets of sensor nodes, where the members of each of those sets together completely cover the monitored area. The intervals of activity are the same for all sets, and only one of the sets is active at any time. The experimental results demonstrate that by using only a subset of sensor nodes at each moment, we achieve a significant energy savings while fully preserving coverage.
1,074 citations
"Genetic algorithm with a new fitnes..." refers background in this paper
...Sensor activity scheduling is addressed in [8], [9], structure of network is addressed in [10], aggregation of data is addressed in [1], [11], data compression in [12] and routing protocols in [13]–[15]....
[...]