Wireless sensor network (WSN) is one of the key enablers for Internet of things (IoT) applications such as smart homes, intelligent manufacturing, agriculture, healthcare monitoring among others. Small sensors are dep...
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ISBN:
(纸本)9781728105703
Wireless sensor network (WSN) is one of the key enablers for Internet of things (IoT) applications such as smart homes, intelligent manufacturing, agriculture, healthcare monitoring among others. Small sensors are deployed in a specific environment to sense and acquire the vital data and transmit to Base Station (BS). Due to resource constraints of the sensors and the need for long lifetime, energy consumption is a challenging issue that directly affects the network lifetime and performance of the IoT applications. In this paper, we present a novel intelligent clustering technique utilizing a computational intelligence technique, namely fuzzy logic, to efficiently improve the network lifetime and performance. In particular, we propose a load balance clustering algorithm (LBCA) that performs load balancing on the selection of cluster head (CH) among all sensors, based on a priority queue, using a fuzzy inference system, to minimize and distribute the energy consumption. In addition, we propose a scheduling algorithm based on TDMA for reducing unnecessary intra- cluster communication that leads to a prolonged lifetime and enhanced performance. Simulations are conducted to evaluate the performance of the proposed fuzzy logic based clustering technique, taking into account the network lifetime in terms of First Node Dead, Half Nodes Dead and End Node Dead, and the network performance in terms of packets sent to BS. Based on the simulation results, the proposed clustering technique has shown significant benefits compared to other conventional solutions, revealing the proficient network lifetime and performance provided by the proposed fuzzy logic based clustering technique.
Continuously monitoring sensor readings is an important building block for many IoT applications. the literature offers resourceful methods that minimize the amount of communication required for continuous monitoring,...
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ISBN:
(纸本)9781728105703
Continuously monitoring sensor readings is an important building block for many IoT applications. the literature offers resourceful methods that minimize the amount of communication required for continuous monitoring, where Geometric Monitoring (GM) is one of the most generally applicable ones. However, GM has unique communication requirements that require specialized network protocols to unlock the full potential of the algorithm. In this work, we show how application and protocol co-design can improve the real-life performance of GM, making it an application of practical value for real IoT deployments. We orchestrate the communication of GM to utilize the properties of a state-of-the-art wireless protocol (Crystal) that relies on synchronous transmissions and is designed for aperiodic traffic, as needed by GM. We bridge the existing gap between the capabilities of the protocol and the requirements of GM, especially in the case of periods of heavy communication. We do so by introducing an in-network aggregation technique relying on latent opportunities for aggregation that we exploit in Crystal's design, allowing us to reliably monitor duplicate sensitive aggregate functions, such as sum, average or variance. Our results from testbed experiments with a publicly available dataset show that the combination of GM and Crystal results in a very small duty-cycle, a 2.2x - 3.2x improvement compared to the baseline and up to 10x compared to previous work. We also show that our in-network aggregation technique reduces the duty-cycle by up to 1.38x.
In this article, we develop a comprehensive framework to characterize the performance of a drone assisted backscatter communication-based Internet of things (IoT) sensor network. We consider a scenario where the drone...
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ISBN:
(纸本)9781728105703
In this article, we develop a comprehensive framework to characterize the performance of a drone assisted backscatter communication-based Internet of things (IoT) sensor network. We consider a scenario where the drone transmits an RF carrier that is modulated by IoT sensor node (SN) to transmit its data. the SN implements load modulation which results in amplitude shift keying (ASK) type modulation for the impinging R F carrier. In order to quantify the performance of the considered network, we characterize the coverage probability for the ground based SN node. the statistical framework developed to quantify the coverage probability explicitly accommodates a dyadic backscatter channel which experiences deeper fades than that of the one-way Rayleigh channel. Our model also incorporates Line of Sight (LoS) and Non-LoS (NLoS) propagation states for accurately modelling large-scale path- loss between drone and SN. We consider spatially distributed SNs which can be modelled using a spatial Binomial Point Process (BPP). We practically implement the proposed system using Software Defined Radio (SDR) and a custom designed SN tag. the measurements of parameters such as noise figure, tag reflection coefficient etc., are used to parametrize the developed framework. Lastly, we demonstrate that there exists an optimal set of parameters which maximizes the coverage probability for the SN.
Traffic congestion in large cities became more intense considering the last years. Basically, this growth is attributed to the wide use of a single mode of transport due to the lack of alternatives capable of efficien...
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