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A Simplified Analog Implementation of Cyclic Shift Chirp Encoding and Decoding for LoRa Communications
(2026-06-01)
Saelim, Nopparut
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Tuwanut, Panwit
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Pornavalai, Chotipat
Currently, LoRa wireless communication technology has become widely adopted in IoT systems due to its long-range capability and low power consumption. However, LoRa is a technology developed by Semtech, which does not disclose the details of the Cyclic Shift Chirp encoding process, a core component of LoRa signals. This lack of transparency prevents users from accessing the physical-layer structure of the signal or freely customizing key parameters such as bandwidth and spreading factor. Although such customization can enhance system flexibility, there is currently no officially disclosed method to achieve it. This research proposes a Cyclic Shift Chirp encoder/decoder circuit built from basic analog components, including adders, subtractors, comparators, and ramp generators, based on a Pulse Width Modulation (PWM) principle. This approach enables researchers and developers to generate LoRa-like signals independently and customize various parameters without introducing limitations. Moreover, the proposed circuit is simple, low-cost, and easy to understand, making it suitable for advanced research, educational experiments, and the design of communication systems that require high flexibility at the LoRa PHY layer.
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EORL: Energy Optimization via Reinforcement Learning in Software-Defined Wireless Sensor Networks
(2024-01-01)
Boonlert, Arnut
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Pornavalai, Chotipat
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Tuwanut, Panwit
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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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A Novel Precomputed Optimal Procrastination Time Interval for Re-Clustering to Maximize Operation Time of Wireless Sensor Networks
(2023-09-01)
Pornavalai, Chotipat
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Tanessakulwattana, Sarayoot
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Chakraborty, Goutam
In wireless sensor networks, the energy consumption of sensors is not uniform over the whole region of deployment. The uneven energy usage occurs because some sensors have to transmit data to farther distances or have to transmit more data packets than others. This leads to a shorter duration of operation because some sensors' energy will deplete fast creating holes in the network. To alleviate this problem, we proposed an algorithm we named Procrastinated Clustering and Multi-Hop Routing (PCMR). To prolong the operation, it will optimally assign sensors with different precomputed procrastination periods to schedule the clustering and routing processes. In PCMR, sensors' clustering and routing intervals depend on their locations in the network with respect to the sink. The algorithm could reduce and balance energy consumption for sensors distributed over a wide area. Procrastination periods are precomputed off-line before deployment. Therefore, it is easy to implement and is efficient, even for a large network for which real-time reorganization would involve transmitting a large number of signaling packets. The results from simulations show that the proposed PCMR algorithm could balance energy usage among sensors, and prolong the network lifetime compared to existing works based on techniques such as adjusting cluster size and/or multi-path transmission.
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A Practical Circuit of Cyclic Chirp Spread Spectrum Modulation for Long Range Communication
(2022-01-01)
Sinchai, Ananta
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Saelim, Nopparut
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Pornavalai, Chotipat
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Wardkein, Paramote
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Tuwanut, Panwit
Various technologies have been utilized in long range communication to satisfy increasing usage of the internet of things in a wide large area. Recently, a modulating technique called cyclic chirp spread spectrum (CSS) modulation has attracted attention due to its low power usage and long-range coverage. However, a cyclic CSS circuit is complex to build. Hence, the objective of the work is to propose a practical circuit of cyclic CSS modulation. The proposed circuit is designed to complicated-free creation. To prove that the presented circuit is pragmatic, a simulation test is performed. The simulation results have illustrated that the designed circuit well generates a chirp modulating signal. Also, the proposed circuit is flexibly configurable.
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Multipath energy balancing for clustered wireless sensor networks
(2019-07-01)
Tanessakulwattana, Sarayoot
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Pornavalai, Chotipat
In wireless sensor networks, sensors at different locations in the field use different energy levels to propagate sensing data back to the sink or base station. This causes unbalanced energy usage among sensors and also lowers the network lifetime. Currently there are several techniques to mitigate this problem, such as deploying multiple sinks, adding more sensors on heavy traffic areas, or managing the size of clusters depending on the distance from sensor to sink. In this paper, we propose a distributed algorithm and protocol called Multipath Energy Balancing (MEB) to mitigate unbalanced energy usage in clustered wireless sensor networks using multi-path and multi-hop, with a transmission power control approach. The network field is divided into regions, where the ratio of inter-region transmission traffic from all cluster head sensors in one region to other cluster head sensors in the two regions in front can be pre-computed and pre-programmed into the sensors to ease sensor deployment. To further prolong network lifetime, we also present a simple heuristic algorithm to procrastinate cluster formation and routing. Simulation results show that MEB can balance energy much better than Energy-efficient Clustering (EC) and Balancing Energy Consumption (BEC) solutions. It also has a longer network lifetime than EC and BEC protocols, especially when the required cluster size is small. Procrastinating cluster formation and routing also can further improve the network lifetime.
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Scanning behavior for efficient energy consumption in delay tolerant network
(2017-11-03)
Tirataworawan, Visarut
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Pornavalai, Chotipat
Delay-Tolerant Network (DTN) is a sparse network of mobile wireless nodes which there is no end-to-end connectivity between nodes. DTN is used to provide communications in the extreme terrestrial environments, mobile environments, or in certain situations that it is impossible for infrastructure network to deliver data. In this paper, we will focus on how to utilize node power consumption in order to maximize the data delivery probability in various DTN environments. The main factor that responsible for the most consuming power is a scanning process to find nearby nodes. If nodes wasted too much energy to scan for other nodes unnecessarily, it would likely miss a transfer opportunity due to not having enough power left. Therefore, the goal is to minimize the amount of energy consumed during scanning process in order to reduce the overall node energy consumption while it can achieve the best delivery probability. The simulations show that node density is inverse to the amount of energy nodes used to scan for its neighbor nodes. The more nodes stay close to each other, the amount of time node uses to scan for others is reduced, hence less power consumption.
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Experimental performance evaluation of LoRaWAN: A case study in Bangkok
(2017-09-05)
Vatcharatiansakul, Nuttakit
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Tuwanut, Panwit
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Pornavalai, Chotipat
The Internet of Things (IoT) is a vision which real-world objects are part of the internet. Every object is uniquely identified, and accessible to the network. There are various types of communication protocol for connect the device to the Internet. One of them is a Low Power Wide Area Network (LPWAN) which is a novel technology use to implement IoT applications. There are many platforms of LPWAN such as NB-IoT, LoRaWAN. In this paper, the experimental performance evaluation of LoRaWAN over a real environment in Bangkok, Thailand is presented. From these experimental results, the communication ranges in both an outdoor and an indoor environment are limited. Hence, the IoT application with LoRaWAN technology can be reliable in limited of communication ranges.
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The fuzzy-based cluster head election algorithm for equal cluster size in wireless sensor networks
(2016-11-18)
Eak-Une, Pichatorn
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Pornavalai, Chotipat
In Wireless Sensor Network (WSNs), clustering technique is widely used to balance energy usages consumed by the sensors. In each round of operation, a number of sensors are chosen to be candidate cluster head (CCHs) with a fixed and predefined probability value. CCHs then compete among themselves to become Cluster Head (CH) based on some criteria such as its remaining residual energy. CH role is rotated among sensors within the network field to balance their residual energy. However, area near to the corner and edge of the network field usually has less number of sensors to be CHs role than sensors located around center of the field. This will create energy holes problem on the area where sensors that needed to be CHs more often than others. Another problem is almost existing cluster head competition methods cannot precisely control the size of clusters in the networks. Depend on the spatial correlation of sensor nodes, this may impact the quality of data aggregation performed by the CHs. In this paper, we propose fuzzy based cluster head election algorithm (called FuzzCHE) to control and maintain cluster size while balance residual energy of sensors and extend the network lifetime. With FuzzCHE, each sensor can dynamically adjust probability that each sensor becomes CCH in each round by fuzzy logic. Comparing with existing cluster head election algorithm, results from simulations show that FuzzCHE can give more precise control of the average operated cluster size in the network to the deployed size required by the application.
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Unequal Initial Energy Assignment in Wireless Sensor Networks
(2015-08-24)
Eak-Une, Pichatorn
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Pornavalai, Chotipat
In multi-hop clustering Wireless Sensor Networks (WSNs), Cluster Head (CH) role is rotated among sensor nodes in order to balance their residual energy usages. However, to forward data from CHs to sink or Base Station (BS), CH nodes that are placed near to the sink consume more energy than CH nodes that are further away. This is known as 'Energy Hole' problem in WSNs. This paper proposes an algorithm called 'Unequal Initial Energy Assignment' or UIEA to determine initial energy for sensors based on their distances to the sink. This unequal energy assignment is to assign different values of initial energy to sensors that belong to different regions in the network field. Since CHs perform data aggregation, this approach allows sensors to operate using same cluster sizes throughout deployed network field, which is one of the important requirements for some specific applications. Performance from simulation shows that UIEA can get more detail and precise data after aggregation by deploying smaller cluster size than average EC cluster size while the Stable Operation Period (SOP) is approximately the same as EC solution.
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Coverage maximization with sleep scheduling for wireless sensor networks
(2015-08-17)
Danratchadakorn, Chanrit
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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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