Abstract:We propose an adaptive interference avoidance scheme that enhances the performance of ZigBee networks by adapting ZigBees' transmissions to measured wireless local area network (WLAN) interference. Our proposed algorithm is based on a stochastic analysis of ZigBee operation that is interfered with by WLAN transmission, given ZigBee and WLAN channels are overlaid in the industrial, scientific, and medical (ISM) band. We assume that WLAN devices have higher transmission power than ZigBee devices. Then, the high … Show more
“…The discrete time Markov chain (DTMC) models to analyze the IEEE 802.15.4 have been studied in [11][12][13][14][15]. Park et al [11] analyzed the throughput and energy consumption under saturated condition and validated their analysis through comparison with simulations.…”
Section: Related Workmentioning
confidence: 99%
“…Lee et al [14] presented an additional carrier sensing (ACS) algorithm for IEEE 802.15.4 WSNs which gives the node one more chance that performs CCA if and only if the second CCA of the device is busy. Chong et al [15] analyzed the performance of throughput and energy consumption of ZigBee sensor networks under wireless local area network (WLAN) interference. However, in [12,14,15], the deferred Wireless Communications and Mobile Computing transmission has not been considered when the remaining slots are not enough to transmit a frame.…”
Section: Related Workmentioning
confidence: 99%
“…Chong et al [15] analyzed the performance of throughput and energy consumption of ZigBee sensor networks under wireless local area network (WLAN) interference. However, in [12,14,15], the deferred Wireless Communications and Mobile Computing transmission has not been considered when the remaining slots are not enough to transmit a frame. Jung et al [13] analyzed throughput of the slotted CSMA/CA for IEEE 802.15.4 with considering deferred transmission that occurs when the remaining slots are not enough to transmit a frame and validated the model through simulations.…”
Section: Related Workmentioning
confidence: 99%
“…where is the probability that a device performs the first CCA when the device is in the backoff procedure [11,15] and is the number of active devices. Then, from (1), we can calculate as…”
Section: Estimation Of the Number Of Active Devices In Ieee 802154 mentioning
We propose a novel method for estimating the number of active devices in an IEEE 802.15.4 network. Here, we consider an IEEE 802.15.4 network with a star topology where active devices transmit data frames using slotted carrier sense multiple access with collision avoidance (CSMA/CA) medium access control (MAC) protocol without acknowledgment. In our proposed method, a personal area network (PAN) coordinator of a network counts the number of events that a transmission occurs and the number of events that two consecutive slots are idle in a superframe duration, and the PAN coordinator broadcasts the information through a beacon frame. Each device can count the number of slots that each device is in the backoff procedure and the number of the first clear channel assessment (CCA) that each device performs whenever it performs the first CCA after the backoff procedure. Then, each device estimates the number of active devices in the network based on these counted numbers and the information from PAN coordinator with the help of an autoregressive moving average (ARMA) filter. We evaluate the performance of our proposed ARMA-based estimation method via simulations where active devices transmit data frames in IEEE 802.15.4 slotted CSMA/CA networks. Simulation results show that our proposed method gives estimation errors of the number of active devices less than 4.501% when the actual number of active devices is varying from 5 to 80. We compare our proposed method with the conventional method in terms of the average and standard deviation for the estimated number of active devices. The simulation results show that our proposed estimation method is more accurate than the conventional method.
“…The discrete time Markov chain (DTMC) models to analyze the IEEE 802.15.4 have been studied in [11][12][13][14][15]. Park et al [11] analyzed the throughput and energy consumption under saturated condition and validated their analysis through comparison with simulations.…”
Section: Related Workmentioning
confidence: 99%
“…Lee et al [14] presented an additional carrier sensing (ACS) algorithm for IEEE 802.15.4 WSNs which gives the node one more chance that performs CCA if and only if the second CCA of the device is busy. Chong et al [15] analyzed the performance of throughput and energy consumption of ZigBee sensor networks under wireless local area network (WLAN) interference. However, in [12,14,15], the deferred Wireless Communications and Mobile Computing transmission has not been considered when the remaining slots are not enough to transmit a frame.…”
Section: Related Workmentioning
confidence: 99%
“…Chong et al [15] analyzed the performance of throughput and energy consumption of ZigBee sensor networks under wireless local area network (WLAN) interference. However, in [12,14,15], the deferred Wireless Communications and Mobile Computing transmission has not been considered when the remaining slots are not enough to transmit a frame. Jung et al [13] analyzed throughput of the slotted CSMA/CA for IEEE 802.15.4 with considering deferred transmission that occurs when the remaining slots are not enough to transmit a frame and validated the model through simulations.…”
Section: Related Workmentioning
confidence: 99%
“…where is the probability that a device performs the first CCA when the device is in the backoff procedure [11,15] and is the number of active devices. Then, from (1), we can calculate as…”
Section: Estimation Of the Number Of Active Devices In Ieee 802154 mentioning
We propose a novel method for estimating the number of active devices in an IEEE 802.15.4 network. Here, we consider an IEEE 802.15.4 network with a star topology where active devices transmit data frames using slotted carrier sense multiple access with collision avoidance (CSMA/CA) medium access control (MAC) protocol without acknowledgment. In our proposed method, a personal area network (PAN) coordinator of a network counts the number of events that a transmission occurs and the number of events that two consecutive slots are idle in a superframe duration, and the PAN coordinator broadcasts the information through a beacon frame. Each device can count the number of slots that each device is in the backoff procedure and the number of the first clear channel assessment (CCA) that each device performs whenever it performs the first CCA after the backoff procedure. Then, each device estimates the number of active devices in the network based on these counted numbers and the information from PAN coordinator with the help of an autoregressive moving average (ARMA) filter. We evaluate the performance of our proposed ARMA-based estimation method via simulations where active devices transmit data frames in IEEE 802.15.4 slotted CSMA/CA networks. Simulation results show that our proposed method gives estimation errors of the number of active devices less than 4.501% when the actual number of active devices is varying from 5 to 80. We compare our proposed method with the conventional method in terms of the average and standard deviation for the estimated number of active devices. The simulation results show that our proposed estimation method is more accurate than the conventional method.
“…The interference avoidance in the frequency domain is also attractive. The most of the research is focused on ZigBee devices and its channel adjustment [19]- [23], but recently [24] proposed that both ZigBee and WiFi change operating frequency.…”
A rapid growth of the wireless communications and heavily occupied spectrum lead to an inevitable interference between the heterogenous systems operating in the same frequency band. Having in mind the development of the Internet of Things (IoT) services and networks and widely present WiFi networks on the one hand, and the fact that these two systems occupy the same 2.4 GHz frequency band on the other hand, it is clear that the control of the interference and the spectrum coordination are of the highest importance. The first step in the interference control is to acquire its properties. Since the simulation of a large IoT network is not entirely possible, due to the numerous factors not known in advance, the interference assessment is performed on the SmartSantander, an IoT testbed, located in Santander, Spain. This paper presents a statistical analysis of the sensor data and describes the interference properties and its influence. These results may be used for the spectrum coordination, together with the neural networks and semantic technologies.
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