Fall is one of the main causes of injuries for the elderly, and fall detection (FD) for senior monitoring has received considerable attention from both the academic community and healthcare industries. In recent years, there has been an increasing interest in using wearable sensors, such as accelerometers to monitor the subject’s body movement and apply Machine Learning (ML) methods to detect and prevent falls. Since it is extremely difficult to collect accelerometer data of real falls during activities of daily living (ADL), researchers tended to rely on simulating falls in well-protected environments. They collected ADLs separately, applied ML algorithms to classify falls and ADLs, and reported very high FD accuracy rates. However, these studies cannot be applied in a real fall context. In this paper, instead of classifying ADL and fall separately, we propose to incorporate fall data within ADL data to obtain more realistic datasets and apply ML to detect falls. Several ML algorithms including CatBoost (CB), Decision Tree (DT), Random Forest (RF), and XGBoost (XGB) were applied to the datasets. Experimental results show a fall detection accuracy of $88.70 \%$. We also extend our work to cover slow fall which, to the best of our knowledge, was not extensively addressed in previous works.

Falls are one of the most common causes of injury among the elderly. As a result, fall detection has received in the last decade considerable attention from both academia and the healthcare industry. Accelerometer data, collected from simulated falls, were widely used with classical machine learning (ML) algorithms as well as with threshold-based methods to identify fall situations that can be used to launch an alert for help. As collecting real fall data is challenging, most of the research papers on fall detection have used limited data which do not reflect the complexity of real fall situations. Fortunately, a comprehensive fall dataset called “Simulated Falls and Daily Living Activities Dataset” has recently become available. This dataset includes 1827 simulated falls of 20 different types. In this paper, we use this dataset to evaluate the possibility of fall detection, more precisely, impact and pre-impact which correspond to fall and pre-fall, respectively. Unlike the classical ML algorithms and threshold-based methods commonly used in previous research works, in this paper, we implement a bidirectional long short-term memory (Bi-LSTM) algorithm which we believe better reflects the impact and pre-impact context as it takes into consideration both backward and forward sequence information at every time step. Our experimental results showed that Bi-LSTM achieves an accuracy of 99.97% and 99.95%, with 99.80% and 99.30% sensitivity, and 100% and 99.99% specificity for fall and pre-fall detections, respectively. These results largely exceed previously published results.

In cognitive radio networks with primary users implementing CSMA/CA, such as 802.11 technologies, when all the primary users are in backoff states, the secondary users may detect primary users as inactive and try to access the channel, which will prolong primary users backoff procedure. This phenomenon is called hidden collision. In this paper, we first demonstrate how primary users' performance is degraded by hidden collision. Then, to protect primary users from excessive hidden collision, we propose a method to prevent secondary users from accessing the time slots during which all active primary users are in backoff states. Numerical examples are provided to show the effectiveness of the proposed solution.

Rapid urbanization has put increasingly pressure on traffic management in urban areas. Conventional traffic signal with fixed or pre-defined variable cycles setting can slightly alleviate the increasing traffic problem, but cannot deal with continuously growing vehicular traffic in rapidly growing urban areas. VANET technology offers a promising solution for better vehicular traffic management in urban area to reduce traffic jam and improve transportation safety. Adaptive Traffic Light Control (ATLC) using VANET has attracted considerable attention from academic community. Unfortunately, most of these existing works used simulated traffic flow and hypothetical intersection architectures which may not reflect the reality of urban area. In this paper, we present a case study based on a specific intersection in the city of Moncton with real traffic data, and propose a new adaptive traffic light control algorithm. Our results show a substantial improvement of traffic throughput and average waiting time in comparison with fixed optimal cycle's time currently used by the city of Moncton and with existing adaptive solutions.

In this paper, we apply VANET capabilities to develop a new adaptive traffic light control algorithm that takes into consideration in real time traffic density and vehicle relative position to the intersection. Our preliminary results show a substantial improvement of traffic throughput and average waiting time in comparison with fixed optimal cycle's time and with existing adaptive solutions.

Abstract In cellular communication systems, in order for a network to keep track of inactive mobile stations (MSs), each inactive MS has to update its location from time to time, called location registration. To lighten the task of tracking inactive MSs, the network divides its cells into groups, called location areas (LAs) and tracks inactive MSs at the LA level: an inactive MS sends a registration message to the network to update its location only when it travels to a new LA. Obviously, the performance of a location area design depends on network traffic and the mobility of MSs. In the paper, we propose a general Markov mobility model for MSs in cellular networks, provide a procedure to automatically estimate the system parameters according to the network traffic, derive the performance of LA design, and provide a clustering algorithm to optimize LA designs. A numerical example is provided to show the effectiveness of the proposed procedures. Copyright © 2009 John Wiley & Sons, Ltd.

In order to save power to prolong battery life of subscriber stations (SSs) in IEEE 802.16e networks, the standard defines a sleep mode for SS. When there is no traffic for an SS to transmit or to receive, the SS switches to sleep mode periodically. The sleep interval is doubled each time until a maximum sleep interval threshold Tmaxis reached. Obviously, the performance of this power saving mechanism depends on the idle period distribution, which is user-specific. In network traffic modeling, it is commonly accepted that frame interarrival times have heavy-tailed distributions. Since heavy-tailed distributions make analysis and design challenging, in this paper we propose to use mixtures of exponentials to approximate heavy-tailed idle times. With a mixture of exponentials approximating the idle times, performance can be explicitly derived and optimized. An online EM algorithm is proposed to fit the mixture of exponential distributions to the idle times. Numerical examples show the effectiveness of the proposed procedures.

Smart antenna is a promising technology to improve the capacity of wireless networks. Implementing smart antenna in WLAN urgently demands new multiple access protocols. In this paper we propose a multiple access protocol for WLAN to effectively support smart antenna. The proposed protocol makes use of the hybrid superframe structure of polling and contention, which is illumed from IEEE 802.11, and distinguishes the stations residing within the AP's broadcast range from those out of the broadcast range. For the stations residing within the AP's broadcast range, the AP uses polling to transmit in broadcasting mode; for those residing out of the AP's broadcast range but in its directional range, the AP uses contention-based method to transmit in beamforming mode. We have evaluated and optimized the protocol performance against its parameters by simulation. To show the efficiency of the proposed protocol, we also compare it with other protocols in average packet delay and throughput. The simulation results show that the proposed protocol performs well; especially, under heavy traffic load, it achieves the smallest average delay and the highest throughput.

The use of smart antenna to extend the coverage range and capacity of wireless local area networks (WLAN) dictates the employment of novel multiple access protocols, with which the access point (AP) can provide access to remote stations. In this paper, we propose an adaptive multiple access protocol for WLAN with smart antenna. To efficiently support the differentiation of the stations residing within the broadcasting coverage range of the AP from those residing out of the range, the proposed protocol uses the hybrid superframe structure of contention-free period and contention period. Moreover, our protocol can adaptively adjust the antenna mode between beamforming and broadcasting according to the beam width and the number of active stations in the service area. Using simulation we compared the adaptive protocol with other non-adaptive ones. The results show that constructing our adaptive protocol by switching among non-adaptive protocols turns out to be the best strategy which allow us to take advantage of the best protocol providing low delay and high throughput.

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The hyper-Erlang model can approximate any non-negative distribution as closely as desired. It can not only characterize field data well, but also facilitate analytically tractable results for performance evaluation. As a result, this model is widely used in telecommunication network modeling. This paper proposes an online algorithm for Gamma mixture distributions, which contain hyper-Erlang models as a special case, and applies the algorithm to Internet traffic modeling. Simulation and experimental results are also provided.

In this paper, we propose a mobile terminal (MT) location registration/update model. In this model, the registration decision is based on two factors-the time elapsed since last call arrival and the distance the MT has traveled since last registration. It is established that the optimal registration strategy can be represented by a curve. Only when the state of the system reaches this curve is a registration performed. In order for an MT to calculate its traveled distance, an interactive implementation scheme and a distance calculation algorithm are developed. When the call interarrival times are independent and geometrically distributed, the proposed model becomes a distance-based model and, in this case, the optimal registration strategy is of threshold structure. For the distance-based model, a single sample path-based ordinal optimization algorithm is devised. In this algorithm, without any knowledge about the system parameters, the MT observes the system state transitions, estimates the ordinal of a set of strategies, and updates the registration strategy adaptively. Since only a single sample path is used, this algorithm can be implemented online. Several numerical examples are provided to compare the proposed model and the existing ones.