Wearable wireless networks (WWNs) offer innovative ways to connect humans and/or objects anywhere, anytime, within an infinite variety of applications. WWNs include three levels of communications: ...on-body, body-to-body and off-body communication. Successful communication in on-body and body-to-body networks is often challenging due to ultra-low power consumption, processing and storage capabilities, which have a significant impact on the achievable throughput and packet reception ratio as well as latency. Consequently, all these factors make it difficult to opt for an appropriate technology to optimize communication performance, which predominantly depends on the given application. In particular, this work emphasizes the impact of coarse-grain factors (such as dynamic and diverse mobility, radio-link and signal propagation, interference management, data dissemination schemes, and routing approaches) directly affecting the communication performance in WWNs. Experiments have been performed on a real testbed to investigate the connectivity behavior on two wireless communication levels: on-body and body-to-body. It is concluded that by considering the impact of above-mentioned factors, the general perception of using specific technologies may not be correct. Indeed, for on-body communication, by using the IEEE 802.15.6 standard (which is specifically designed for on-body communication), it is observed that while operating at low transmission power under realistic conditions, the connectivity can be significantly low, thus, the transmission power has to be tuned carefully. Similarly, for body-to-body communication in an indoor environment, WiFi IEEE 802.11n also has a high threshold of end-to-end disconnections beyond two hops (approximatively 25 m). Therefore, these facts promote the use of novel technologies such as 802.11ac, NarrowBand-IoT (NB-IoT) etc. as possible candidates for body-to-body communications as a part of the Internet of humans concept.
The fifth-generation (5G) and beyond 5G (B5G) wireless networks introduced massive machine-type communications (mMTC) to cope with the growing demand of massive Internet of Things (IoT) applications. ...However, the heterogeneous characteristics of massive IoT and diverse quality of service (QoS) requirements may lead to severe interference that could degrade the expected QoS of the cellular ecosystem. Therefore, this article studies the impact of interference caused by mMTC connections. We theoretically model the intercell interference (ICI) minimization problem for the existing orthogonal multiple access (OMA) technique and propose its corresponding solution. Furthermore, we jointly solve the ICI and the cochannel interference minimization problem for the IoT users when the nonorthogonal multiple access (NOMA) technique is used. For the proposed OMA and NOMA schemes, we design a cooperative scheduler to reduce the impact of such interference. The results show that our proposed schemes provide up to 58%, 75%, and 100% more improvements in terms of user's data rates, energy consumption, and connection density, respectively.
The main focus of this research article is the evaluation of selected voice over Internet protocol (VoIP) solutions in wireless mesh network (WMN) scenarios. While WMNs have self-healing, ...self-forming, and dynamic topology features, they still pose challenges for the implementation of multimedia applications such as voice in various scenarios. Therefore, various solutions to make WMN more suitable for VoIP application have been proposed in the scientific literature. In this work, we have extensively explored a set of applicable scenarios by conducting experiments by means of a network simulator. The following scenarios were selected as the most representatives for performance evaluation: first responders, flooded village, remote village, and platoon deployment. Each selected scenario has been studied under six sub-scenarios corresponding to various combinations of the IEEE 802.11g, 802.11n, 802.11s, and 802.11e standards; the G.711 and G.729 codecs; and the ad hoc on demand distance vector (AODV) and hybrid wireless mesh protocol (HWMP) routing protocols. The results in terms of quality of service (measured with the mean opinion score rating scale), supported by the analysis of delay, jitter and packet loss, show that 802.11g integration with both VoIP codecs and AODV routing protocol results in better VoIP performance as compared to most other scenarios. In case of 802.11g integration with 802.11s, VoIP performance decreases as compared to the other sub-scenarios without 802.11s. The results also show that 802.11n integration with 802.11e decreases VoIP performance in larger deployments. We conclude the paper with some recommendations in terms of combinations of those standards and protocols with a view to achieve a higher quality of service for the given scenarios.
In the context of wireless sensor networks, energy prediction models are increasingly useful tools that can facilitate the power management of the wireless sensor network (WSN) nodes. However, most ...of the existing models suffer from the so-called fixed weighting parameter, which limits their applicability when it comes to, e.g., solar energy harvesters with varying characteristics. Thus, in this article we propose the Adaptive LINE-P (all cases) model that calculates adaptive weighting parameters based on the stored energy profiles. Furthermore, we also present a profile compression method to reduce the memory requirements. To determine the performance of our proposed model, we have used real data for the solar and wind energy profiles. The simulation results show that our model achieves 90-94% accuracy and that the compressed method reduces memory overheads by 50% as compared to state-of-the-art models.
A swarm of autonomous and heterogeneous drones has many benefits in various scenarios e.g. search and rescue, disaster management, agriculture, delivery and logistics, mapping and surveying, ...environmental monitoring, etc. However, the presence of obstacles in the environment poses challenges to communication between drones, including network coverage, received signal power, latency, and power consumption. To improve the drones' communication in real-time scenarios, reconfigurable intelligent surfaces (RIS) can be used. RIS is a promising technology for empowering millimeter-waves and sub-millimeter waves communication. It also can provide improved communication links with significantly higher received signal strength in non-line-of-sight situations, which should be taken into account by drones to decide when and with which other drone(s) to perform computation offloading. To this end, we provide two federated learning-based computation offloading strategies through direct and indirect communications. These approaches are based on an advanced rating technique including some key computation and communication parameters. The core of the algorithm also involves two separate deep learning models that are helpful to efficiently transfer and update the decreased model weights, drones' properties, angle of arrival, and angle of departure. Simulation results show that the efficiency of the proposed approaches are superior to a reference strategy in terms of energy consumption by −32%, latency −18%, throughput +50%, and cost of communication and computation by −35%.
Device to device (D2D) communication has attracted enormous attention for future cellular networks which helps to increase the cellular capacity, improve the user throughput, and extend the battery ...lifetime of user equipments (UEs) by reusing the spectrum resources. However, D2D devices provide interferences in the system while reusing the resources. Proper control of interferences helps to increase the performance of the overall system. Adaptive power allocation among cellular and D2D users contributes to providing an efficient interference management system. In this paper, we propose an online power allocation method, i.e., multi-armed bandit solver for D2D communication. We explore the proposed method to improve the system throughput and D2D throughput as well. We incorporate the set of states for this learning algorithm with the appropriate number of system-defined variables, which increases the observation space and consequently improve the balance of spectrum usage. Finally, we compare our proposed work with existing distributed reinforcement learning and random allocation of resources. Simulation results depict that the proposed resource allocation method outperforms the existing works regarding overall system throughput as well as D2D throughput by efficiently controlling the interference levels.
Energy harvesting technologies such as miniature power solar panels and micro wind turbines are increasingly used to help power wireless sensor network nodes. However, a major drawback of energy ...harvesting is its varying and intermittent characteristic, which can negatively affect the quality of service. This calls for careful design and operation of the nodes, possibly by means of, e.g., dynamic duty cycling and/or dynamic frequency and voltage scaling. In this context, various energy prediction models have been proposed in the literature; however, they are typically compute-intensive or only suitable for a single type of energy source. In this paper, we propose Linear Energy Prediction "LINE-P", a lightweight, yet relatively accurate model based on approximation and sampling theory; LINE-P is suitable for dual-source energy harvesting. Simulations and comparisons against existing similar models have been conducted with low and medium resolutions (i.e., 60 and 22 min intervals/24 h) for the solar energy source (low variations) and with high resolutions (15 min intervals/24 h) for the wind energy source. The results show that the accuracy of the solar-based and wind-based predictions is up to approximately 98% and 96%, respectively, while requiring a lower complexity and memory than the other models. For the cases where LINE-P's accuracy is lower than that of other approaches, it still has the advantage of lower computing requirements, making it more suitable for embedded implementation, e.g., in wireless sensor network coordinator nodes or gateways.
NarrowBand Internet of Things (NB-IoT) is an emerging cellular IoT technology that offers attractive features for deploying low-power wide-area networks suitable for implementing massive machine-type ...communications. NB-IoT features include, e.g., extended coverage and deep penetration for massive connectivity, longer battery-life, appropriate throughput, and desired latency at lower bandwidth. Regarding the device energy consumption, NB-IoT is mostly underestimated for its control and signaling overheads, which calls for a better understanding of the energy consumption profiling of an NB-IoT radio transceiver. With this aim, this work presents a thorough investigation of the energy consumption profiling of the radio resource control (RRC) communication protocol between an NB-IoT radio transceiver and a cellular base station. Using two different commercial off the shelf NB-IoT boards and two mobile network operators (MNOs) NB-IoT test networks operational at Tallinn University of Technology, Estonia, we propose an empirical baseline energy consumption model. Based on comprehensive analyses of the profile traces from the widely used BG96 NB-IoT module operating in various states of the RRC protocol, our results indicate that the proposed model accurately depicts the baseline energy consumption of an NB-IoT radio transceiver while operating at different coverage class levels. The evaluation errors of our proposed model vary between 0.33% and 15.38%.
Good quality of single droplet image acquisition in imaging flow cytometry (IFC) is crucial for a wide range of biological analyses. Recently, there have been significant advances in droplet ...microfluidic data analysis; however, acquiring blur-free single object images is still a great challenge because of the tradeoff between high flow rate and hardware setup complexity and cost. State-of-the-art hardware setups for blur-free single image acquisition are often complex, cumbersome, and not portable, limiting their suitability for point-of-care diagnostics. Moreover, motion blur and duplicate droplet image acquisition can occur with flow rate variation. To address these issues, this paper proposes a lightweight imaging pipeline for acquiring blur-free single droplet images for portable applications; this pipeline is capable of acquiring every single droplet image. While most of the existing literature focuses on complex hardware setups, utilizing high frame rate cameras that are not cost effective and complex optical solutions for droplet focusing, our pipeline utilizes minimum hardware and a lightweight algorithm for detecting, counting, and acquiring single object images from the video stream. The proposed pipeline was evaluated experimentally using videos of fast-moving droplets in which the input fluid flow rate was as high as <inline-formula> <tex-math notation="LaTeX">67.7~\mu </tex-math></inline-formula>L/min. The proposed pipeline achieves 100% counting accuracy on the tested videos and 2 ms, 25 ms and 10 ms processing time for each droplet on a desktop PC, single-board computer Raspberry Pi-4, and Nvidia Jetson Nano, respectively. This yields a maximum of 500, 40, and 100 blur free detected droplets per second (DPS), respectively. The Jetson Nano implementation, achieving 100 DPS with processing time of 10 ms, is faster than existing similar studies and fast enough for the target application. The results suggest that the proposed lightweight pipeline is suitable for efficient single object image acquisition in IFC on an embedded portable platform.
In the context of green communication and energy-efficiency in wireless communication, this paper investigates distributed estimation algorithms in an energy-constrained wireless sensor network and ...proposes an energy-efficient distributed leader selection algorithm. The existing state-of-the-art diffusion algorithm and the recently introduced distributed leader selection algorithm are investigated. To evaluate the energy consumption of the algorithms, their respective algorithmic complexity, and number of operations and information exchanges are derived and compared. The obtained values are used as a basis to estimate the execution time and energy consumption of the algorithms. We propose and introduce the energy-efficient distributed leader selection algorithm which retains the performance of the existing leader selection algorithm while reducing the complexity and energy consumption. For the simulations, the algorithms are mapped to widely used wireless sensor network hardware architectures (MSP430 and RSL10). The numerical results show that the proposed algorithm is able to decrease the energy consumption of the network by 32%-53% and can extend the network lifetime by 14%-46% as compared with the diffusion and the distributed leader selection algorithms.
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