Abstract-Under a multirate network scenario, the IEEE 802.11 DCF MAC fails to provide airtime fairness for all competing stations since the protocol is designed for ensuring max-min throughput fairness. As such, the maximum achievable throughput by any station gets bounded by the slowest transmitting peer. In this paper, we present an analytical model to study the delay and throughput characteristics of such networks so that the rate anomaly problem of IEEE DCF multirate networks could be mitigated. We call our proposal Time Fair CSMA (TFCSMA) which utilizes an interesting baseline property for estimating a target throughput for each competing station so that its minimum contention window could be adjusted in a distributed manner. As opposed to the previous work in this area, TFCSMA is ideally suited for practical scenarios where stations frequently adapt their data rates to changing channel conditions. In addition, TFCSMA also accounts for packet errors due to the time varying properties of the wireless channel. We thoroughly compare the performance of our proposed protocol with IEEE 802.11 and other existing protocols under different network scenarios and traffic conditions. Our comprehensive simulations validate the efficacy of our method toward providing high throughput and time fair channel allocation.

This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen’s temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

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In Long Range Wide Area Network (LoRaWAN), the data rate of the devices can be adjusted to optimize the throughput by changing the spreading factor. However, the adaptive data rate has to be carefully utilized because the collision probability, which directly affects the throughput, is changed according to the use of spreading factors. Namely, the greater the number of devices using the same spreading factor, the greater the probability of collision, resulting in a decrease of total throughput. Nevertheless, in the current system, the only criteria to determine the data rate to be adjusted is a link quality. Therefore, contention-aware adaptive data rate should be designed for the throughput optimization. Here, the number of devices which can use a specific data rate is restricted, and accordingly the optimization problem can be regarded as constrained optimization. To find an optimal solution, we adopt the gradient projection method. By adjusting the data rate based on the retrieved set of optimal data rate, the system performance can be significantly improved. The numerical results demonstrate that the proposed method outperforms the comparisons regardless of the number of devices and is close to the theoretical upper bound of throughput.

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