Driven by advances in signal processing and multiuser detection (MUD) technologies, it has become possible for a wireless node to simultaneously receive multiple signals from other transmitters. In order to take full advantage of MUD in multi-packet reception (MPR) capable wireless networks, it is highly desirable to make the compound signals from multiple transmitters more separable on its constellation at the receiver by coordinating both the transmit power level and carrier phase offsets of the transmitters. In this article, we propose a feedback-based transmit power and carrier phase adjustment scheme that estimates the symbol energy and the carrier phase offset for each transmitter’s received signal, computes the optimal received power level and carrier phase shift to maximize the minimum Euclidean distance between the constellation points, and finally feeds the optimal transmit power level and phase shift information back to the transmitters. We then evaluate the performance of the proposed transmit power and carrier phase adjustment scheme and subsequently show that the proposed scheme significantly reduces the error probability in a multiuser communication system having MPR capability.

Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.

Cloud radio access network (C-RAN) is a promising mobile wireless sensor network architecture to address the challenges of ever-increasing mobile data traffic and network costs. C-RAN is a practical solution to the strict energy-constrained wireless sensor nodes, often found in Internet of Things (IoT) applications. Although this architecture can provide energy efficiency and reduce cost, it is a challenging task in C-RAN to utilize the resources efficiently, considering the dynamic real-time environment. Several research works have proposed different methodologies for effective resource management in C-RAN. This study performs a comprehensive survey on the state-of-the-art resource management techniques that have been proposed recently for this architecture. The resource management techniques are categorized into computational resource management (CRM) and radio resource management (RRM) techniques. Then both of the techniques are further classified and analyzed based on the strategies used in the studies. Remote radio head (RRH) clustering schemes used in CRM techniques are discussed extensively. In this research work, the investigated performance metrics and their validation techniques are critically analyzed. Moreover, other important challenges and open research issues for efficient resource management in C-RAN are highlighted to provide future research direction.

In this paper, we study the network of unmanned aerial vehicles (UAVs) which acts as a relay between a base station and remotely deployed Internet of Things devices. UAVs have received much attention for connecting remotely located networks/objects, but there is a lack of study on multi-hop relay transmission schemes over UAV networks. It is particularly challenging because of the their high mobility and limited resources, to name a few. In this regard, we propose an energy-efficient, cooperative multi-hop relay scheme for UAVs. We formulate the optimal multi-hop transmission scheduling problem to minimizes the power consumption of UAVs without violating the given time constraint. We convert the initial formulation into a low-complex iterative algorithm, making it suitable for an online scheduling. Also, we propose a buffer prediction and correction scheme to initiate the multi-hop transmission scheduling without having the perfect buffered data information. Evaluation results show that the proposed scheme can energy-efficiently schedule the multi-hop transmissions over dynamic UAVs without violating the time constraints.

A heterogeneous network (HetNet) can actively utilize the spectrum reuse with low power consumption, and thus is promising for the next-generation cellular networks. However, there are some technical challenges to be overcome in order for HetNets to be practical, and we address the following two in this paper. One is how to formulate the association and resource scheduling problem in a way that an optimal solution can be found in a reasonable amount of time, and the other is how to accommodate varying users' demand. In order to minimize the power consumption and to satisfy varying users' QoS (Quality of service) requirements, we propose a low-complex, distributed association and resource allocation scheme. By taking a cost-based approach, we first separate a non-convex joint association and resource allocation problem into two subproblems. The channel allocation and base station assignment problem is then relaxed so that the problem becomes tractable. For the power allocation problem, we introduce a low-complex iterative algorithm by using the decomposition theory. The evaluation results show that the proposed solution can maintain the overall power consumption minimized while satisfying the QoS requirements.

We consider the power saving mechanism for 802.11ah on large-scale sensor networks. Power saving mode allows sensor nodes to switch to low-power state while APs buffer incoming frames for them. Also, 802.11ah introduced the TIM and page segmentation scheme to cope with a large number of nodes. Although it is a powerful tool for reducing contention and increasing sleep intervals, the advantage comes at the expense of additional energy waste, which becomes exacerbated as the number of nodes increases. This paper aims at minimizing such energy waste and enhancing energy efficiency which is critical to large-scale 802.11ah networks. We propose a method that selectively and dynamically changes the membership of nodes and rearranges their traffic to maximize overall sleeping intervals without causing delay to data delivery. To this end, we propose a temporary membership change scheme and a traffic scheduling algorithm which reduce overall power consumption. Also, to make the problem tractable and scalable, we apply a relaxation technique and devise a low-complex scheduling algorithm, respectively, of which performance is comparable to optimum. Evaluation results show that the proposed scheme can enhance energy efficiency by decreasing the number of active nodes by up to 37.8% compared to 802.11ah.