Recently, intelligent reflecting surface (IRS) has emerged as a promising technology, which can be applied in mobile edge computing (MEC) systems to achieve higher data transmission efficiency and reliability, by providing a reflective channel. Concurrently, rate-splitting multiple access (RSMA), as an innovative technology, is increasingly utilized in MEC systems to enhance data offloading efficiency and facilitate a better integration of computation and communication. In this paper, an IRS enabled MEC system with RSMA under user mobility is considered. Based on this system model, we propose an optimization problem that is aimed at maximizing the system's data transmission rate by jointly optimizing the RSMA power allocation and the IRS phase shift parameters. Although traditional optimization methods can be utilized to solve the considered problem, it is quite time consuming since the optimization methods are often iterative algorithms. To design low complexity algorithm, we propose a deep reinforcement learning (DRL) approach that can efficiently make good decisions quickly after training. Numerical results indicate that, compared to the baseline algorithms, the proposed DRL-based IRS-aided offloading algorithm under RSMA protocol achieves superior system performance.

The healthcare industry faces challenges due to rising treatment costs, an aging population, and limited medical resources. Remote monitoring technology offers a promising solution to these issues. This article introduces an innovative adaptive method that deploys an ultrawideband (UWB) radar-based Internet of Medical Things (IoMT) system to remotely monitor elderly individuals’ vital signs and fall events during their daily routines. The system employs edge computing for prioritizing critical tasks and a combined cloud infrastructure for further processing and storage. This approach enables monitoring and telehealth services for elderly individuals. A case study demonstrates the system’s effectiveness in accurately recognizing high-risk conditions and abnormal activities, such as sleep apnea and falls. The experimental results show that the proposed system achieved high accuracy levels, with a mean absolute error (MAE) ± standard deviation of absolute error (SDAE) of 1.23± 1.16 bpm for heart rate (HR) detection and 0.22 ± 0.27 bpm for respiratory rate (RR) detection. Moreover, the system demonstrated a recognition accuracy of 90.60% for three types of falls (i.e., stand, bow, squat to fall), one daily activity, and No Activity Background. These findings indicate that the radar sensor provides a high degree of accuracy suitable for various remote monitoring applications, thus enhancing the safety and well-being of elderly individuals in their homes.

This study focuses on an intelligent reflecting surface (IRS) enabled simultaneous wireless information and power transfer (SWIPT) system with the coexistence of legitimate users (LUs) and Eavesdroppers (Eves). The main objective is to jointly optimize the transmit beamforming and artificial noise covariance matrix at the access point, the phase shift matrix at the IRS, and the power splitting ratio at the LUs, to maximize the system’s min-secrecy rate. Due to the imperfect channel state information of Eves, an outage rate constraint is contained. The formulated problem is a challenging nonconvex optimization problem since it involves nonconvex objective function and constraints, and the outage rate constraint does not have simple closed form expression. To address this problem, an algorithm based on the alternating optimization method is proposed, which breaks down the nonconvex problem into three subproblems. The algorithm employs several techniques to solve these subproblems. Specifically, the outage rate constraint is approximated using the Bernstein-type inequality. And the Taylor formula, semi-definite relaxation, and successive convex approximation methods are employed to transform the nonconvex subproblems into convex ones. Simulation results demonstrate the effectiveness of the proposed algorithm compared to baseline algorithms under different conditions.

Recently, the cybertwin-driven intelligent internet of vehicles has received widespread consideration in modern smart cities which makes it possible to run high dimensional, low-latency tolerating, and computational-intensive tasks on the vehicles. Thanks to the development in mobile edge computing, the so-called vehicular edge computing allows mobile vehicles to offload their tasks to the road-side unit or hybrid access point due to the limited computation capability. In this paper, we consider a cybertwin-driven internet of vehicle system that provides computing services for mobile vehicles in local area network or wide area network aided with multi-intelligent reflecting surfaces. Based on this system model, we investigate an optimization problem to jointly maximize the sum of data rate in wide area network, and the sum of energy utilities of vehicles. However, in the proposed system model, it is complicated to design the optimal phase, scheduling and offloading decision policy. To solve this issue, we propose a block coordinate descent and deep reinforcement learning based intelligent IoV computing policy. Numerical results have verified that the proposed algorithm can achieve better IoV computing performance compared with four relative benchmark algorithms.

Recently, the intelligent reflecting surface (IRS) is an emerging and promising technology for achieving higher spectrum and energy efficiency in wireless communication systems. In this paper, we consider a wireless powered mobile edge computing (MEC) network that is equipped with an IRS. The IRS is able to provide a reflecting channel to enhance the offloading capability for edge users. Based on this system model, we investigate an optimisation problem to maximize the sum of users' utilities, which jointly consider the energy efficiency, time latency, and price of offloading computations. With task offloading, power limited users can complete the computational tasks even when they face data-intensive workloads. However, in a dynamic system, it is complicated to design the optimal offloading decision strategy. To tackle this problem, we propose a deep reinforcement learning (DRL) based approach. In the designed algorithm, in order to get a better reward, the agent chooses a near optimal solution to adjust the workload partitions, the time allocation, and IRS parameters according to the dynamic channel environment and the random arrival of task workload. Numerical results show that the proposed DRL based IRS-aided offloading algorithm can achieve better system performance compared with that without IRS and the relative benchmark algorithms.

With the fast development of wearable IoT devices, their applications are becoming more and more pervasive, ranging from social networking, payment, and navigation to health and activity monitoring. The security of the communication between these devices is essential to protect the transmitted sensitive information from tampering and eavesdropping. With the integration of accelerometers into wearable IoT devices, the gait-based biometric cryptography technology has emerged as a data securing tool for wearables. This article proposes a lightweight noise-based group key generation method, which utilizes the noise signals imposed on the raw acceleration signals to generate an M-bit key with high randomness and bit generation rate. Moreover, a signed sliding window coding (SSWC)-based common feature extraction method was designed to extract the common feature for sharing the generated M-bit key among devices worn on different body parts. Finally, a fuzzy vault-based group key distribution system was implemented and evaluated using a public data set. The performed comprehensive analysis of the proposed key generation and distribution method proved that the binary keys generated via the introduced noise-based procedure have high entropy and can pass both the NIST and Dieharder statistical tests with high efficiency. The experimental results obtained prove the robustness of the proposed SSWC-based common feature extraction method in terms of the similarity and discriminability of intra- and inter-class features, respectively.

In a wireless body area network (WBAN), walking movements can result in rapid channel fluctuations, which severely degrade the performance of transmission power control (TPC) schemes. On the other hand, these channel fluctuations are often periodic and are time-synchronized with the user's gait cycle, since they are all driven from the walking movements. In this paper, we propose a novel gait-cycle-driven transmission power control (G-TPC) for a WBAN. The proposed G-TPC scheme reinforces the existing TPC scheme by exploiting the periodic channel fluctuation in the walking scenario. In the proposed scheme, the user's gait cycle information acquired by an accelerometer is used as beacons for arranging the transmissions at the time points with the ideal channel state. The specific transmission power is then determined by using received signal strength indication (RSSI). An experiment was conducted to evaluate the energy efficiency and reliability of the proposed G-TPC based on a CC2420 platform. The results reveal that compared to the original RSSI/link-quality-indication-based TPC, G-TPC reduces energy consumption by 25% on the sensor node and reduce the packet loss rate by 65%.

Energy efficiency is a key issue in wireless body area networks (WBANs). A number of transmission power control (TPC) schemes have been developed to improve the efficiency of transmission, which is one of the most energy consuming operations in WBAN. To save energy, these schemes only probe the link quality from the received data packets. However, due to large intervals between data packets and fast dynamic on-body link characteristics in WBAN, the obtained link information is usually outdated. In this case, the performance of the current TPC scheme is poor. This paper proposes an accelerometer-assisted TPC (AA-TPC) scheme, which exploits the periodic fluctuations of link qualities to improve the transmission energy efficiency. Consider the relationship between link quality and body movement, AA-TPC makes transmissions at ideal channel points that are identified by using the local accelerometer. We first conduct experiments to investigate the correlation between periodic movements and link quality. Then, we propose an algorithm to locate the time point in each period with the best link quality to transmit packets. The specific transmission power is then determined by the feedback information from the receiver. Finally, we evaluate the energy efficiency of AA-TPC based on a CC2420 platform in both a periodic scenario (without any aperiodic movement to break the periodicity) and a realistic scenario (which has aperiodic movements 20% of the time). The results show that about 26.4% and 18% of total energy consumption can be saved on average in the periodic and realistic scenarios, respectively.