Energy Efficient Communication Scheme Research Articles

XplAInable: Explainable AI Smoke Detection at the Edge

Wild and forest fires pose a threat to forests and thereby, in extension, to wild life and humanity. Recent history shows an increase in devastating damages caused by fires. Traditional fire detection systems, such as video surveillance, fail in the early stages of a rural forest fire. Such systems would see the fire only when the damage is immense. Novel low-power smoke detection units based on gas sensors can detect smoke fumes in the early development stages of fires. The required proximity is only achieved using a distributed network of sensors interconnected via 5G. In the context of battery-powered sensor nodes, energy efficiency becomes a key metric. Using AI classification combined with XAI enables improved confidence regarding measurements. In this work, we present both a low-power gas sensor for smoke detection and a system elaboration regarding energy-efficient communication schemes and XAI-based evaluation. We show that leveraging edge processing in a smart way combined with buffered data samples in a 5G communication network yields optimal energy efficiency and rating results.

Deep-Learning-Based Resource Allocation for Transmit Power Minimization in Uplink NOMA IoT Cellular Networks

For Internet of Things (IoT) networks, it is important to develop energy-efficient communication schemes to extend the operating life of battery-powered IoT devices. Additionally, non-orthogonal multiple access (NOMA) can utilize frequency resources more efficiently than orthogonal multiple access, making it more suitable to support massive connectivity of IoT users. Motivated by these facts, we consider uplink NOMA IoT cellular networks and develop two novel algorithms that jointly optimize sub-band allocation and transmit power control to minimize the total transmit power of all users and the maximum transmit power among all users’ transmit power, respectively, while meeting the minimum required data rate for all users. Specifically, we propose a novel two-step approach that sequentially performs sub-band assignment and transmit power control for each IoT user, in which a genetic algorithm-based method is applied for sub-band assignment whereas unsupervised learning (USL) implemented as deep neural network (DNN) models is utilized for transmit power control. Moreover, we propose loss functions that can achieve an appropriate balance between power minimization and rate constraint satisfaction in the process of training. Extensive simulations are performed to evaluate the performance of the proposed algorithm in various aspects, and we show that our proposed two-step algorithm can approach the optimal performance achievable through an exhaustive search with much lower computational complexity.

Sustainable Operations in IoT by Combining Spatiotemporal Data Correlation With Silent Symbol Based Communication Strategy

We exploit spatiotemporal correlation in sensor data and redundant binary number system based representation for encoding data, where the number of 0&#x00A0;s in the encoded data is significantly larger than the other symbols. Next, using a hybrid FSK-ASK modulation/demodulation technique with a non-coherent receiver, we have designed an energy-efficient communication scheme where the transmitter is kept silent during the transmission of the dominant symbol in the encoded messages. In contrast to the conventional techniques of data reduction and aggregation which exploit temporal and spatial correlation, our technique does not require dropping any data value while generating significantly more transmission energy savings. Our approach is particularly useful for sustainable energy-efficient communications in IoT applications like environment monitoring, agriculture, etc., where dropping any data value may not be desirable. Simulation results on different real-life sensor data sets using several popular low-cost, low data-rate transceivers like the RFM TR 1000 show about <inline-formula><tex-math notation="LaTeX">$90.06\% - 90.86\%$</tex-math></inline-formula> savings in transmitter energy expenditure over conventional BFSK, and <inline-formula><tex-math notation="LaTeX">$15\% - 20\%$</tex-math></inline-formula> savings at receiver. Simulation results also demonstrate a proportionately reduced <inline-formula><tex-math notation="LaTeX">$CO_{2}$</tex-math></inline-formula> footprint generation of <inline-formula><tex-math notation="LaTeX">$0.12 - 0.14$</tex-math></inline-formula> mg/day only, when using the RFM TR 1000 radio. These savings are considerably higher than those generated with the existing techniques.

System-Centric Computation Energy Efficiency for Distributed NOMA-Based MEC Networks

Energy-efficient communication is one of the most pertaining issues in realizing mobile edge computing (MEC) networks. How to effectively use non-orthogonal multiple access (NOMA) to realize energy efficient MEC networks has been of high interest and calls for in-depth study. In particular, there has been yet no work to study the energy efficient communication scheme from system perspective in this area. In this work, we focus on the design of a system-centric computation energy efficiency (EE) scheme in a NOMA-based MEC network. The system computation EE is maximized by jointly optimizing the computing frequencies of the MEC server and the local users, the offloading time, the transmit powers of local users, as well as the execution time for the local users and the MEC server. Employing the fractional programming theory and introducing auxiliary variables, we develop an efficient Dinkelbach-based iterative scheme to tackle the formulated non-linear fractional programming problem. For obtaining instrumental insights, we further derive closed-form expressions for the partial solutions based on Karush-Kuhn-Tucker conditions. Simulation results are provided to verify the quick convergence of the proposed iterative algorithm, to obtain insights, as well as to demonstrate the superiority of the proposed resource allocation scheme in terms of the system computation EE by comparing it with benchmark schemes.

An Energy-Efficient Communication Scheme for Collaborative Mobile Clouds in Content Sharing: Design and Optimization

This paper addresses the energy efficiency issue for content sharing with collaborative mobile clouds (CMC). We start by maximizing the data rate of cellular transmissions under the maximum transmit power constraint of the cellular users, to obtain the optimal beamforming vectors. Using these vectors, we propose a water filling based data segmentation approach for content distribution. Furthermore, within the CMC, we design cost-effective resource allocation and power control mechanisms for device-to-device communications. Through performance comparisons, we disclose that our proposed scheme outperforms some previous study in terms of delay and energy consumption per mobile terminal, which further validates the effectiveness of our design.

EPKF: Energy Efficient Communication Schemes Based on Kalman Filter for IoT

The Internet of Things (IoT) has been recognized as the next technological revolution. It faces two challenges: 1) how to achieve energy efficient communication for the battery constrained devices and 2) how to connect a very large number of devices to the Internet with low latency, high efficiency, and reliability. To address these problems, this paper proposes two methods based on Kalman filter (KF), termed as extensions of predicable KF (EPKF). They locally reduce the unnecessary transmission (access) of end devices to the network (Internet) utilizing the spatial and temporal correlations with low algorithmic overhead. Each transmitting device (TD) independently controls its transmission using the temporal correlation; and the receiving device (RD) exploits the spatial correlation among the TDs to further improve the reconstruction quality. The reconstruction problem in the RD is nonlinear. To reduce the computation complexity, an in-depth analysis of the local estimate error is conducted and the approximated linear solutions are thereupon obtained. They are fundamental methods applicable to any IoT monitored/controlled physical system that can be modeled as a linear state space representation. The pedestrian-position application is used as a case study to demonstrate the efficiency in the simulation. Remarkably, the EPKF methods using the linear combinations of the local estimates from multiple TDs reduce the transmission rate to 10%, while achieving the same reconstruction quality as using KF in the traditional manner.

Enhanced Fully Generalized Spatial Modulation for the Internet of Underwater Things.

A full design of the Internet of Underwater Things (IoUT) with a high data rate is one of the greatest underwater communication difficulties due to the unavailability of a sustainable power source for the battery supplies of sensor nodes, electromagnetic spread weakness, and limited acoustic waves channel bandwidth. This paper presents a new energy-efficient communication scheme named Enhanced Fully Generalized Spatial Modulation (EFGSM) for the underwater acoustic channel, where the different number of active antennas used in Fully Generalized Spatial Modulation (FGSM) is combined with multiple signal constellations. The proposed EFGSM enhances energy efficiency over conventional schemes such as spatial modulation, generalized spatial modulation, and FGSM. In order to increase energy and spectral performance, the proposed technique conveys data bits not just by the number of active antenna’s index as in the existing traditional FGSM, but also using the type of signal constellation to increase the data bit rate and improve power saving without increasing the receiver’s complexity. The proposed EFGSM uses primary and secondary constellations as indexes to carry information, they are derived from others by geometric interpolation signal space. The performance of the suggested EFGSM is estimated and demonstrated through Monte Carlo simulation over an underwater acoustic channel. The simulation results confirm the advantage of the suggested EFGSM scheme not just regarding energy and spectral efficiency but also concerning the average bit error rate (ABER).

A Review of An Energy Efficient Communication Scheme for Low Power Wireless Network

A Review of An Energy Efficient Communication Scheme for Low Power Wireless Network

A Group-Less and Energy Efficient Communication Scheme Based on Wi-Fi Direct Technology for Emergency Scenes

Recently, smartphones are commonly equipped with Wi-Fi direct modules, which can be used to support infrastructure-less communications. Considering an emergency scene, communication infrastructures are damaged or unable to operate. Affected people in the emergency site cannot communicate with each other and with the outside world until rescue personnel establishes an emergency network for them. In this paper, we aim to provide an emergency communication method based on Wi-Fi direct technology. Originally, Wi-Fi direct capable devices rely on a Wi-Fi direct group mechanism to operate. However, we observe that forming Wi-Fi direct groups may not be appropriated in the emergency scenario. Besides, Wi-Fi direct capable devices carried by affected people should operate in power saving mode when emergency. In this paper, we model the target scenario as an emergency Wi-Fi direct device scheduling (EWDS) problem. We then propose centralized and distributed schemes to solve the EWDS problem. The proposed schemes schedule devices’ active and sleep timings with the guarantee that all devices can join the network within a bounded time and devices can exchanges packets with each other effectively. Furthermore, by the proposed distributed schemes, devices can operate locally and unsynchronized without a central controller. We evaluate the proposed schemes by simulations and prototyping implementation, and the results demonstrate the effectiveness of our designs.

MRBNS: a new energy-efficient communication scheme in low power wireless networks

A new energy-efficient communication scheme has been proposed based on a novel source coding technique along with the idea of silent communication, so that the frequencies of occurrences of different symbols in the encoded message become very asymmetric. Keeping the symbol period with the highest frequency of occurrence as silent during transmission, we achieve substantial savings of transmitter and receiver energies by using a hybrid FSK-ASK modulation/demodulation technique. We also design the corresponding protocols for transmission and reception of messages and evaluate their performances to compare with other existing techniques. Our results demonstrate that for additive white Gaussian noise in channels, on an average, the transmitter side energy is reduced by about 53%, while at the receiver side there is about 17.2% savings. Due to the savings of both transmitter and receiver energies, our proposed scheme is particularly suitable for multi-hop communication in low power wireless networks.

Network Coding for Energy Efficient Transmission in Wireless Body Area Networks

Wireless Body Area Networks (WBANs) is a technology that has revolutionized the health-care industry by allowing remote monitoring, early detection and prevention of diseases. Patients can be remotely monitored thanks to implant devices in the body or by placing nodes on different parts of the body. Often the deployed sensor nodes are constraint by limited battery sources and are required to continuously collect the data and transfer it to the sink node, thus requiring energy efficient communication schemes. In addition, in poor channel conditions, this data collection and transfer results in retransmission thus wasting useful energy. This work presents a cooperative network coding based transmission technique for spectrum and energy efficiency in WBANs. The bit error rate (BER) of the network coded path in comparison to direct communication approach is explored. The effect of WBAN path loss due to different node positions is also discussed. It is observed that node position greatly affects path loss and received power which in turn affect the BERs. Simulation results show that network coded cooperative communication strategy in WBAN channel with and without combining outperforms as compared to direct communication.

An energy-efficient wireless communication scheme using Quint Fibonacci number system

In this paper, an energy-efficient communication scheme for low-power wireless networks is proposed which is based on encoding the messages in a new number system, called the Quint Fibonacci number system QFNS, followed by using a concept of Silent Communication. The proposed scheme employs a simple hybrid modulation/demodulation technique using non-coherent FSK and ASK, which leads to the design of a low-power, low-cost wireless communication system that will particularly be suitable for remote/rural healthcare applications. We show that with Additive White Gaussian Noise AWGN in channel, our scheme offers about 39.6% energy savings at the transmitter and about 52% energy savings at receiver on an average over the conventional Binary Frequency Shift Keying BFSK system, for equal likelihood of all possible strings of binary messages. However, simulation results with different types of messages from various healthcare and agricultural sensor data applications show that we get about 29.82% to 36.23% energy savings at the transmitter and about 56.42% to 64.29% energy savings at the receiver over conventional BFSK.

A Novel Energy Efficient Scheme With a Finite-Rate Feedback Channel

In this letter, we propose an energy efficient communication scheme with a limited feedback rate for Rayleigh fading channels. First, we employ the available feedback words to quantize the channel-to-noise ratio (CNR) range and divide it into different regions. Then, we formulate the average energy efficiency (EE) for a given number of CNR regions using a fixed power and rate in each region. After that, we propose an offline optimization algorithm to compute the energy efficient CNR thresholds of each region and the associated transmission powers that maximize the EE. Finally, we evaluate the proposed scheme through simulations, by comparing the EE performance and effective achievable capacity against the case of perfect-CSI.

A Tutorial for Energy-efficient Communication for XMPP-based Internet of Things

This paper introduces the Extended Messaging Presence Protocol and shows how it is adapted to support the small and power-constrained devices that will make up the Internet of Things by requiring low energy consumption. Soon, everything in the world will be connected via the Internet, from simple machines to humans. Every machine that we can see in our surroundings, such as the air conditioner, heater, or even home electricity meter will have a communication and network hardware module installed to be a part of the Internet of Things. So, machines will produce data to be delivered to service users of the Internet of Things who needed the data. In this communication and network paradigm, it is considered that the Extended Messaging Presence Protocol is a good candidate for connecting machines and delivering messages among them. In addition, the Extended Messaging Presence Protocol provides real-time communication and network accessibility to the devices in private network as well. However the Presence message of the Extended Messaging Presence Protocol causes a large amount of network traffic overhead. Therefore, small, battery-powered Internet of Things devices would consume a great deal of energy in a short period of time. So this paper provides an energy-efficient communication scheme based on the Extended Messaging Presence Protocol for resource constrained devices.

CNS: a new energy efficient transmission scheme for wireless sensor networks

We present in this paper a new energy-efficient communication scheme called CNS (Compression with Null Symbol) that combines the power of data compression and communication through silent symbol. The concept of communication through silent symbol is borrowed from the energy efficient schemes proposed in Sinha (Proceedings of 6th IEEE consumer communications and networking conference (CCNC), Las Vegas, pp. 1---5, 2009), Ghosh et al. (Proceedings of 27th IEEE international performance computing and communications conference (IPCCC), USA, pp. 85---92, 2008), and Sinha and Sinha (Proceedings of international conference on distributed computing and internet technologies (ICDCIT), LNCS, pp. 139---144, 2008). We show that the average theoretical energy saving at the transmitter by CNS is 62.5%, assuming an ideal channel and for equal likelihood of all possible binary strings of a given length. Next, we propose a transceiver design that uses a hybrid modulation scheme utilizing FSK and ASK so as to keep the cost/complexity of the radio devices low. Considering an additive white gaussian noise (AWGN) channel and a non-coherent detection based receiver, CNS shows a saving in transmitter energy by 30% when compared to binary FSK, for equal likelihood of all possible binary strings of a given length. Simultaneously, there is a saving of 50% at the receiver for all types of data modulation due to halving of the transmitted data duration, compared to binary encoding. In contrast, RBNSiZeComm proposed in Sinha (Proceedings of 6th IEEE consumer communications and networking conference (CCNC), Las Vegas, pp. 1---5, 2009), TSS proposed in Ghosh et al. (Proceedings of 27th IEEE international performance computing and communications conference (IPCCC), USA, pp. 85---92, 2008) and RZE proposed in Sinha and Sinha (Proceedings of international conference on distributed computing and internet technologies (ICDCIT), LNCS, pp. 139---144, 2008) generate average transmitter energy savings of about 41, 20, and 35.2%, respectively. Also, at the receiver side, while RBNSiZeComm does not generate any saving, TSS and RZE produce about 36.9 and 12.5% savings on an average, respectively. Considering certain data types that may occur in the context of some wireless sensor networks (WSN) based applications (e.g., remote healthcare, agricultural WSNs, etc.), our simulation results demonstrate that for AWGN noisy channels, the transmitter side savings vary from about 33---50% on an average, while for RBNSiZeComm, this saving is about 33---61% on the same data set (Sinha in Proceedings of 6th IEEE consumer communications and networking conference (CCNC), Las Vegas, pp. 1---5, 2009). Thus, taking into account the low cost/complexity of the proposed transceiver, these results clearly establish that CNS can be a suitable candidate for communication in low power wireless sensor networks, such as in remote healthcare applications, body area networks, home automation, WSNs for agriculture and many others.

On the distribution of runs of ones in binary strings

In this paper, we derive the number of binary strings which contain, for a given i k , exactly i k runs of 1’s of length k in all possible binary strings of length n , 1 ≤ k ≤ n . Such a knowledge about the distribution pattern of runs of 1’s in binary strings is useful in many engineering applications — for example, data compression, bus encoding techniques to reduce crosstalk in VLSI chip design, computer arithmetic using redundant binary number system and design of energy-efficient communication schemes in wireless sensor networks by transformation of runs of 1’s into compressed information patterns, among others. We present, here, a generating function based approach to derive a solution to this counting problem. Our experimental results demonstrate that, for most commonly used file formats, the observed distributions of exactly i k runs of length k , 1 ≤ k ≤ n , closely follow the theoretically derived distributions, for a given n . For n = 8 , we find that the experimentally obtained values for most file formats agree within ± 5 % of the theoretically obtained values for all i k runs of length k , 1 ≤ k ≤ n . Also, the root mean square (RMS) values of these deviations across all file types studied in this paper are less than 5% for n = 8 . In view of these facts, the results presented in this paper could be useful in various application domains, like the ones mentioned above.