Heuristic methods to maximize network lifetime in directional sensor networks with adjustable sensing ranges

Mohamadi, Hosein; Salleh, Shaharuddin; Razali, Mohd Norsyarizad

doi:10.1016/j.jnca.2014.07.038

Cited by 42 publications

(35 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, a considerable amount of work on coverage in directional sensor networks (DSNs) has been reported in the literature [ 9 ]. In [ 10 ], the authors designed two greedy-based scheduling algorithms that aim to select the appropriated sensor direction and sensing range in a way to meet the requirements of the target coverage problem and at the same time maximize the network lifetime. However, the performance of the algorithms is extremely dependent on the closeness of the initial candidates to the optimal solution.…”

Section: Previous Workmentioning

confidence: 99%

A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks

Ming

Miao

Leung

2015

Sensors

View full text Add to dashboard Cite

Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches.

show abstract

Section: Previous Workmentioning

confidence: 99%

A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks

Ming

Miao

Leung

2015

Sensors

View full text Add to dashboard Cite

show abstract

“…The objective of the MNLAR problem is two fold: (1) perform energy-efficient scheduling by activating and deactivating nodes periodically, and (2) select the active nodes and adjust their sensing ranges to ensure that every target is covered. This problem has been formulated as an optimization problem in the form of Integer Programming [9], [30], [31], Linear Programming [32]- [34], and Voronoi Graphs [35], [36]. This problem is NP-complete [30] making it difficult to perform in real-time.…”

Section: B Maximum Network Lifetime Problemmentioning

confidence: 99%

“…[37] by developing a learning automatabased algorithm to find the optimal cover sets. Additionally, the MNLAR problem was extended to include directional (e.g., camera) sensor networks [34].…”

Section: B Maximum Network Lifetime Problemmentioning

confidence: 99%

POSE: Prediction-Based Opportunistic Sensing for Energy Efficiency in Sensor Networks Using Distributed Supervisors

Hare

Gupta

Wettergren

2018

IEEE Trans. Cybern.

View full text Add to dashboard Cite

This paper presents a distributed supervisory control algorithm that enables opportunistic sensing for energy-efficient target tracking in a sensor network. The algorithm called Prediction-based Opportunistic Sensing (POSE), is a distributed node-level energy management approach for minimizing energy usage. Distributed sensor nodes in the POSE network self-adapt to target trajectories by enabling high power consuming devices when they predict that a target is arriving in their coverage area, while enabling low power consuming devices when the target is absent. Each node has a Probabilistic Finite State Automaton which acts as a supervisor to dynamically control its various sensing and communication devices based on target's predicted position. The POSE algorithm is validated by extensive Monte Carlo simulations and compared with random scheduling schemes. The results show that the POSE algorithm provides significant energy savings while also improving track estimation via fusion-driven state initialization.

show abstract

“…The adjusting technique enables sensors to save energy when they are covering targets positioned in their vicinity since power consumption of sensors depends on the distance between sensors and targets. This technique is applied to the networks where sensors have multiple sensing ranges [23]. This paper attempts to find a solution to the target coverage problem and, simultaneously, extend the network lifetime by means of both techniques of power saving (i.e., scheduling sensors and adjusting sensing ranges).…”

Section: Related Workmentioning

confidence: 99%

A new learning automata-based approach for maximizing network lifetime in wireless sensor networks with adjustable sensing ranges

et al. 2015

Self Cite

View full text Add to dashboard Cite

Heuristic methods to maximize network lifetime in directional sensor networks with adjustable sensing ranges

Cited by 42 publications

References 25 publications

A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks

A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks

POSE: Prediction-Based Opportunistic Sensing for Energy Efficiency in Sensor Networks Using Distributed Supervisors

A new learning automata-based approach for maximizing network lifetime in wireless sensor networks with adjustable sensing ranges

Contact Info

Product

Resources

About