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Author: search.filters.author.Pornavalai, ChotipatSubject: search.filters.subject.Wireless Sensor Networks

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    EORL: Energy Optimization via Reinforcement Learning in Software-Defined Wireless Sensor Networks
    (2024-01-01)
    Boonlert, Arnut
    ;
    Pornavalai, Chotipat
    ;
    Tuwanut, Panwit
    ;
    Tanessakulwattana, Sarayoot
    A wireless sensor network is a collection of sensors placed in a particular area to collect and transmit data to the base station or sink. They usually have batteries as the primary power sources. If they work for a long time, their energy will be exhausted. Replacing the battery may not be cost-effective compared to developing an algorithm that optimizes energy efficiency to extend the network lifetime. This work optimizes the energy consumption of wireless sensor networks by adaptively selecting an optimal routing path in a Software-defined Wireless Sensor Networks (SDWSN) environment. A concept of the energy balance among nodes according to the current network status by the SDWSN controller using Reinforcement Learning (RL) is introduced. We propose energy optimization via reinforcement learning (EORL) for SDWSN using a minimum energy reward function and state design that considers energy consumption. The EORL algorithm then identifies the node that requires attention and selects the most energy-efficient path for that node. The performance of the EORL shows that it has a more extended network lifetime compared with other RL solutions.
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    Coverage maximization with sleep scheduling for wireless sensor networks
    (2015-08-17)
    Danratchadakorn, Chanrit
    ;
    Pornavalai, Chotipat
    Sleep scheduling mechanisms have been widely used in wireless sensor networks so as to extend the lifetime of networks. Sensors are able to decide to be either in active or sleep mode to save the energy. Sensing coverage area is an important factor for some applications such as Intrusion Detection. It is necessary to have the full-sensing-covering set of active nodes on these applications. In this paper, we propose the Coverage Maximization with Sleep Scheduling protocol (CMSS) which is a decentralized protocol and maximize sensing coverage of the network. In our proposed solution, the area of network is divided into grid cells. Each sensor creates a neighbor table and transforms into cell-value table. These tables are used to make decision which mode it should be on each sensor. Simulation results show that CMSS not only consumes less overhead energy than MSCR, but also has a lower number of selected active nodes. Besides, communication range of sensors does not affect to the efficiency of networks like LDCC which exploits hop count information.
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    Adaptive multi-hop routing for wireless sensor networks
    (2013-09-09)
    Tanessakulwattana, Sarayoot
    ;
    Pornavalai, Chotipat
    ;
    Chakraborty, Goutam
    A large portion of energy-aware routing protocol for wireless sensor networks are cluster-based. In cluster based approach, energy at the cluster head nodes are drained more rapidly compared to other member nodes. Dynamically change cluster heads periodically could partialy mitigate this problem, but clusters that are far from base station still suffer from large amount of energy for directly transmit their cluster data back to base station. Multi-hop routing was introduced to reduce energy dissipation of cluster heads that far away from base station by relaying data through nearer cluster heads. However it may overload cluster heads that are near the base station. In this paper, we propose an adaptive multi-hop hierarchical routing approach where member nodes in cluster may send their data, based on distance information, to cluster head or to base station directly to reduce energy dissipation of cluster heads. This decision is independent at each node which makes this approach highly distributed. Simulation results show that the proposed routing protocol has longer node lifetime than the original LEACH and M-LEACH protocol. © 2013 IEEE.