An Optimal Node Scheduling for Flat Wireless Sensor Networks

Nakamura, Fabíola G.; Quintão, Frederico Paiva; Menezes, Gustavo Campos; Mateus, Geraldo Robson

doi:10.1007/978-3-540-31956-6_56

Cited by 28 publications

(15 citation statements)

References 8 publications

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“…Some important parameters shown as follows are used in the simulation experiments: (1) for all ∈ do (2) Evaluate the node's importance ; (3) Decide the size of neighborhood ; (4) Create its candidate-node set ( ); (5) end (6) if the network is broken by a failed node ∈ (7) if nodes in ( ) can replace in terms of (15) (8) Select an optimal node from ( ); (9) else if local MCT of satisfies the network's performance (10) Decide the radius of the regional recovered region; (11) Find the recovery region; (12) else (13) Restart global optimization / * Algorithm 1 * /; (14) end if (15) …”

Section: Simulation Resultsmentioning

confidence: 99%

“…However, once the above constraint condition cannot be satisfied, how to construct fully connected network with optimal coverage rate is the key issue. Nakamura et al [11] transformed the full-connection coverage problem into an ILP model and solved it by the commercial optimization package CPLEX [12]. By comparing WSNs' performances gotten from an ILP model and evolutionary algorithms, Quintão et al [13] indicated that population-based method could achieve reasonable solutions for WSNs with a considerably lower computational cost.…”

Section: Introductionmentioning

confidence: 99%

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A Hierarchical Scheduling Scheme in WSNs Based on Node-Failure Pretreatment

Liu

Guo

Chen

et al. 2015

International Journal of Distributed Sensor Networks

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One of the most important challenges in designing wireless sensor networks is how to recover a broken network within a very short time as the active nodes are failed or out-of-energy. Focusing on this problem, a hierarchical scheduling scheme based on node-failure pretreatment is proposed, in which the global optimization method is used to find the minimum connected tree and the local multilayer recovery algorithm is used to find a candidate sensor node instead of the failed one. Three highlights of this scheme are as follows: (1) The importance of sensor nodes is defined in terms of their locations in minimum connected tree and coverage acreage. (2) The neighborhood radius of failed sensor node varies with its importance, and then its candidate-node set is dynamically constructed. (3) A novel multilayer recovery strategy including node recovery and regional recovery is presented. Simulation results show that hierarchical scheduling scheme finds the optimal candidate sensor node in less time to make the repaired network with lowest energy consumption. Though the less sensor nodes are activated, the network lifetime is slightly shorter. Moreover, this scheme can be applied in the problem that the communication radius of sensor node is less than two times of its sensing radius.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hierarchical Scheduling Scheme in WSNs Based on Node-Failure Pretreatment

Liu

Guo

Chen

et al. 2015

International Journal of Distributed Sensor Networks

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“…The MILP formulation of Nakamura et al [17] achieves this by minimizing the total energy usage with respect to the coverage requirements. Similarly, the model of Cardei et al [18] selects active subsets by allocating their active durations of time to the periods so that the network lifetime, which is defined as the sum of the active time durations over the horizon, is maximized, and a given number of targets are covered in every period.…”

Section: Related Workmentioning

confidence: 99%

An efficient heuristic for placement, scheduling and routing in wireless sensor networks

et al. 2010

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“…These communications consume energy, may produce network congestion or link collision around the central node and are very susceptible to central node failure but the result of the centralized scheduling is generally efficient. For instance, in [9] a dynamic mixed integer linear programming (MILP) model is presented to solve a coupled coverage and connectivity dynamic problem (CCDP) in flat WSNs. A good example for distributed, random sleeping algorithm can be found in [10] where nodes make local decisions on whether to sleep or to join a forwarding backbone, to ensure measurement and communications.…”

Section: Scheduling Techniquesmentioning

confidence: 99%

Optimal Period Length for the CGS Sensor Network Scheduling Algorithm

Bergmann

Molnár

Gönczy

et al. 2010

2010 Sixth International Conference on Networking and Services

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Abstract-In Wireless Sensor Networks (WSN), a tradeoff between the network lifetime (limited by energy of sensors) and redundancy of actively sensing and communicating sensors (implicated by coverage requirements for the measured area) has to be established, typically in an over-deployed environment. This is achieved by scheduling algorithms which periodically alternate the state of sensors between "asleep" and "awake". Obviously, the period length of the periodical synchronized scheduling affects the network performance and lifetime. Controlled Greedy Sleep algorithm is a quasi-optimal synchronized sensor scheduling algorithm which increases network lifetime while maintaining correct functionality, based on local decisions of sensors. This paper investigates the optimization of the period length of this algorithm and highlights best practices with simulations. Studies have been performed within a ring topology and in random square topology.

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An Optimal Node Scheduling for Flat Wireless Sensor Networks

Cited by 28 publications

References 8 publications

A Hierarchical Scheduling Scheme in WSNs Based on Node-Failure Pretreatment

A Hierarchical Scheduling Scheme in WSNs Based on Node-Failure Pretreatment

An efficient heuristic for placement, scheduling and routing in wireless sensor networks

Optimal Period Length for the CGS Sensor Network Scheduling Algorithm

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