On the deployment of wireless data back-haul networks

Liu, Xin; Mohapatra, Pradyumna Kumar

doi:10.1109/twc.2007.348339

Cited by 40 publications

(22 citation statements)

References 21 publications

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“…The goals of our simulation studies are: (1) to demonstrate the difficulty in obtaining feasible solutions for the K-connected DPAP through a purely random process, (2) to test the strength of the proposed operators at improving the performance of MOEA/D at dealing with the K-connected DPAP and (3) to demonstrate the effectiveness of the proposed DPAP-specific MOEA/D-RH against the popular constrained NSGA-II in several WSN instances, giving the trade-off of the objectives and a variety of feasible network design choices.…”

Section: Resultsmentioning

confidence: 99%

“…We assume a perfect medium access control, such as SMAC [29], which ensures that there are no collisions at any sensor during data communication and we adopt the simple but relevant path loss communication model as in [2]. In this model, the transmit power level that should be assigned to a sensor i to reach a sensor j is [2,6] is the path loss exponent and b = 1 is the transmission quality parameter. The energy loss due to channel transmission is d a ij , where d ij is the Euclidean distance between sensors i and j.…”

Section: System Model and Assumptionsmentioning

confidence: 99%

“…Sensors are small electronic devices with limited energy, memory and transmit power capabilities, which in some sensor network applications are also limited in number because of their high cost [2]. A typical goal of these network designs is to form a long lived WSN, such that the sensors, using their sensing capabilities and wireless transceivers, effectively cover a region of interest and forward important information to a common collection point, usually referred to as a data sink.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective K-connected Deployment and Power Assignment in WSNs using a problem-specific constrained evolutionary algorithm based on decomposition

Konstantinidis

Yang

2011

Computer Communications

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a b s t r a c tThe K-connected Deployment and Power Assignment Problem (DPAP) in WSNs aims at deciding both the sensor locations and transmit power levels, for maximizing the network coverage and lifetime objectives under K-connectivity constraints, in a single run. Recently, it is shown that the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is a strong enough tool for dealing with unconstraint real life problems (such as DPAP), emphasizing the importance of incorporating problem-specific knowledge for increasing its efficiency. In a constrained Multi-objective Optimization Problem (such as K-connected DPAP), the search space is divided into feasible and infeasible regions. Therefore, problem-specific operators are designed for MOEA/D to direct the search into optimal, feasible regions of the space. Namely, a DPAP-specific population initialization that seeds the initial solutions into promising regions, problem-specific genetic operators (i.e. M-tournament selection, adaptive crossover and mutation) for generating good, feasible solutions and a DPAP-specific Repair Heuristic (RH) that transforms an infeasible solution into a feasible one and maintains the MOEA/D's efficiency simultaneously. Simulation results have shown the importance of each proposed operator and their interrelation, as well as the superiority of the DPAP-specific MOEA/D against the popular constrained NSGA-II in several WSN instances.

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Section: Resultsmentioning

confidence: 99%

Section: System Model and Assumptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-objective K-connected Deployment and Power Assignment in WSNs using a problem-specific constrained evolutionary algorithm based on decomposition

Konstantinidis

Yang

2011

Computer Communications

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“…Following this idea, some research works, such as Liu and Mohapatra 21 and Guo et al, 22 have addressed the linear sensor deployment problem, where nodes can be placed anywhere along a continuous pipeline. However, nodes can locate segmentally in boxcars for train monitoring to meet the requirements above.…”

Section: Sensor Deployment For Lifetime Maximizationmentioning

confidence: 99%

Maximizing the lifetime of wireless sensor networks in trains for monitoring long-distance goods transportation

Jin

Kong

et al. 2017

International Journal of Distributed Sensor Networks

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One key issue in designing battery-powered wireless sensor networks is to properly control the energy consumption of the sensor nodes in order to prolong their operation time (i.e. lifetime). In this article, we present a real-life application of wireless sensor networks in trains to monitor the goods conditions in a long-distance transportation. We study the wireless sensor network deployment problem in developing a monitoring system with the goal of maximizing the network lifetime under constraints derived from the real application scenario. The key technical problem to solve is to determine the sensor placement and the transmission level for each sensor node, as well as the appropriate number of sensor nodes. We first formulate the problem with a realistic discrete power model as a mixed integer linear programming problem. Then, a two-step efficient deployment heuristic is proposed to satisfy these constraints step by step. The evaluation results indicate that the proposed heuristic performs almost the same as the optimal mixed integer linear programming solution. Moreover, the wireless sensor network with appropriate number of nodes can improve its lifetime up to 10.6% for a train with 80 boxcars. We also discussed a tested experiment in a laboratory environment, as well as the real implementation of the whole monitoring system.

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“…(3) The set of pre-defined potential positions is denoted by P = {p 1 Therefore, we have to place a relay ζ r on any one of the potential positions from ComSet(τ i ) to ensure connectivity of τ i . (7) W denotes the set of relay nodes, which provides k-connectivity to all targets and ω i denotes the set of relay nodes that those are providing k-connectivity to τ i .…”

Section: Terminologies and Proposed Workmentioning

confidence: 99%

Genetic Algorithm for k-Connected Relay Node Placement in Wireless Sensor Networks

Gupta¹,

Kuila

Jana³

2015

Advances in Intelligent Systems and Computing

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Wireless Sensor Networks (WSNs) are widely used for many applications including health care, environment monitoring, underground mines, and so on. In WSN, deployment of relay nodes to cover specific region or target is an important issue. In a target-based WSN, it is important that all the targets must be covered by sensor nodes, and the sensor nodes are connected with the backbone network. In this paper, we propose two algorithms for relay node placement which provide k-connectivity of the sensor nodes. The first algorithm is based on Genetic Algorithm (GA), and the second one is based on greedy approach. We have also to extensively simulate both the algorithms to study their performance.

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On the deployment of wireless data back-haul networks

Cited by 40 publications

References 21 publications

Multi-objective K-connected Deployment and Power Assignment in WSNs using a problem-specific constrained evolutionary algorithm based on decomposition

Multi-objective K-connected Deployment and Power Assignment in WSNs using a problem-specific constrained evolutionary algorithm based on decomposition

Maximizing the lifetime of wireless sensor networks in trains for monitoring long-distance goods transportation

Genetic Algorithm for k-Connected Relay Node Placement in Wireless Sensor Networks

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