Reducing Tyre Wear Emissions of Automated Articulated Vehicles through Trajectory Planning

Effective emission control technologies and eco-friendly propulsion systems have been developed to decrease exhaust particle emissions. However, more work must be conducted on non-exhaust traffic-related sources such as tyre wear. The advent of automated vehicles (AVs) enables researchers and automotive manufacturers to consider ways to further decrease tyre wear, as vehicles will be controlled by the system rather than by the driver. In this direction, this work presents the formulation of an optimal control problem for the trajectory optimisation of automated articulated vehicles for tyre wear minimisation. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one to minimise tyre wear in fixed time cases. Specific boundaries and constraints are applied to the problem to ensure the vehicle's stability and the feasibility of the solution. According to the results, a small increase in the journey time leads to a significant decrease in the mass loss due to tyre wear. The employment of articulated vehicles with low powertrain capabilities leads to greater tyre wear, while excessive increases in powertrain capabilities are not required. The conclusions pave the way for AV researchers and manufacturers to consider tyre wear in their control modules and come closer to the zero-emission goal.

How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks

As the rapid growth of mobile users and Internet-of-Everything devices will continue in the upcoming decade, more and more network capacity will be needed to accommodate such a constant increase in data volumes (DVs). To satisfy such a vast DV increase, the implementation of the fifth-generation (5G) and future sixth-generation (6G) mobile networks will be based on heterogeneous networks (HetNets) composed of macro base stations (BSs) dedicated to ensuring basic signal coverage and capacity, and small BSs dedicated to satisfying capacity for increased DVs at locations of traffic hotspots. An approach that can accommodate constantly increasing DVs is based on adding additional capacity in the network through the deployment of new BSs as DV increases. Such an approach represents an implementation challenge to mobile network operators (MNOs), which is reflected in the increased power consumption of the radio access part of the mobile network and degradation of network energy efficiency (EE). In this study, the impact of the expected increase of DVs through the 2020s on the EE of the 5G radio access network (RAN) was analyzed by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G BS implementation and operation scenarios and for rural, urban, dense-urban and indoor-hotspot device density classes (areas). The results of analyses reveal a strong influence of increasing DV trends on standardized data and coverage EE metrics of 5G HetNets. For every device density class characterized with increased DVs, we here elaborate on the process of achieving the best and worse combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches. This elaboration is further extended on the analyses of the impact of 5G RAN instant power consumption and 5G RAN yearly energy consumption on values of standardized EE metrics. The presented analyses can serve as a reference in the selection of the most appropriate 5G BS deployment and operation approach, which will simultaneously ensure the transfer of permanently increasing DVs in a specific device density class and the highest possible levels of data and coverage EE metrics.

Evaluating heating, ventilation, and air-conditioning systems toward minimizing the airborne transmission risk of Mucormycosis and COVID-19 infections in built environment

This ongoing global pandemic of the COVID-19 has generated a significant international concern for our respiratory health. For instance, the breakout of the COVID-19 pandemic was directly linked to the spread of infectious particles in indoor environments between humans, underlining the significance of rigorous and effective actions to limit the transmission of diseases. Recently, Mucormycosis infections in COVID-19 patients have been identified. This investigation aims to investigate potential infection control HVAC solutions for indoor environments, as well as their core mechanisms for reducing infectious disease risk through simulation models of a valid building in a hot climatic region. Considering recent international recommendations, the investigation relies on a methodology of testing a validated building energy model to several systems in the light of infectious diseases prevention. All proposed models are exposed to cost analysis in line with carbon emissions, and indoor thermal conditions. The analysis outlined through parametric simulations, the effectiveness of the proposed DOAS in supplying 100% fresh ventilation air and enhancing the control of the indoor relative humidity simultaneously. Finally, through an enviro-economic assessment, the study concluded that the DOAS model reduced the CO₂ emissions to 691 tons, with a potential of reducing HVAC and whole-building energy use by 37% and 16%, respectively in the hot arid climate, with a return on investment of about 6%.

An Open-Source Wireless Sensor Node Platform with Active Node-Level Reliability for Monitoring Applications

In wireless sensor networks, the quality of the provided data is influenced by the properties of the sensor nodes. Often deployed in large numbers, they usually consist of low-cost components where failures are the norm, even more so in harsh outdoor environments. Current fault detection techniques, however, consider the sensor data alone and neglect vital information from the nodes’ hard- and software. As a consequence, they can not distinguish between rare data anomalies caused by proper events in the sensed data on one side and fault-induced data distortion on the other side. In this paper, we contribute with a novel, open-source sensor node platform for monitoring applications such as environmental monitoring. For long battery life, it comprises mainly low-power components. In contrast to other sensor nodes, our platform provides self-diagnostic measures to enable active node-level reliability. The entire sensor node platform including the hardware and software components has been implemented and is publicly available and free to use for everyone. Based on an extensive and long-running practical experiment setup, we show that the detectability of node faults is improved and the distinction between rare but proper events and fault-induced data distortion is indeed possible. We also show that these measures have a negligible overhead on the node’s energy efficiency and hardware costs. This improves the overall reliability of wireless sensor networks with both, long battery life and high-quality data.

ESCOVE: Energy-SLA-Aware Edge-Cloud Computation Offloading in Vehicular Networks

The vehicular network is an emerging technology in the Intelligent Smart Transportation era. The network provides mechanisms for running different applications, such as accident prevention, publishing and consuming services, and traffic flow management. In such scenarios, edge and cloud computing come into the picture to offload computation from vehicles that have limited processing capabilities. Optimizing the energy consumption of the edge and cloud servers becomes crucial. However, existing research efforts focus on either vehicle or edge energy optimization, and do not account for vehicular applications’ quality of services. In this paper, we address this void by proposing a novel offloading algorithm, ESCOVE, which optimizes the energy of the edge–cloud computing platform. The proposed algorithm respects the Service level agreement (SLA) in terms of latency, processing and total execution times. The experimental results show that ESCOVE is a promising approach in energy savings while preserving SLAs compared to the state-of-the-art approach.

Induced Effect of Environmental Regulation on Green Innovation: Evidence from the Increasing-Block Pricing Scheme

With increasing constraints on resources and the environment, it is of great practical importance to discover and utilize the induced effect of green technology through market-based tools, in order to simultaneously realize economic development and ecological sustainability. Based on unique patent data from 1999 to 2013, this paper examines the induced effect of China’s increasing-block electricity pricing scheme (IBP) on energy-efficient patents and checks whether the effect is neutral or biased. Furthermore, the quality of the induced patents is identified. The results reveal that increased green innovation is strongly related to the IBP scheme. In addition, the induced effect is biased towards green technology such that, apart from autonomous technological advances, the biased effect of IBP induced two more energy-efficient patents per hundred technological patents. However, the quality of the induced innovation is relatively low: compared to high-quality inventions, low-quality utility models showed greater and more significant growth due to the IBP. Our paper provides quantitative insight into the impact of the IBP and indicates that a reasonable pricing scheme can benefit both the environment and the economy.

A Blockchain-Based Multi-Mobile Code-Driven Trust Mechanism for Detecting Internal Attacks in Internet of Things

A multitude of smart things and wirelessly connected Sensor Nodes (SNs) have pervasively facilitated the use of smart applications in every domain of life. Along with the bounties of smart things and applications, there are hazards of external and internal attacks. Unfortunately, mitigating internal attacks is quite challenging, where network lifespan (w.r.t. energy consumption at node level), latency, and scalability are the three main factors that influence the efficacy of security measures. Furthermore, most of the security measures provide centralized solutions, ignoring the decentralized nature of SN-powered Internet of Things (IoT) deployments. This paper presents an energy-efficient decentralized trust mechanism using a blockchain-based multi-mobile code-driven solution for detecting internal attacks in sensor node-powered IoT. The results validate the better performance of the proposed solution over existing solutions with 43.94% and 2.67% less message overhead in blackhole and greyhole attack scenarios, respectively. Similarly, the malicious node detection time is reduced by 20.35% and 11.35% in both blackhole and greyhole attacks. Both of these factors play a vital role in improving network lifetime.

An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks

In wireless sensor networks (WSNs), detection and report of continuous object, such as forest fire and toxic gas leakage, is one of the major applications. In large-scale continuous object tracking in WSNs, there might be many source nodes simultaneously, detecting the continuous object. Each nodes reports its data to both a base station and mobile workers in the industry field. For communication between the source nodes and a mobile worker, sink location service is needed to continuously notify the location of the mobile worker. But, as the application has a large number of sources, it causes a waste of energy consumption. To address this issue, in this paper, we propose a two-phase sink location service scheme. In the first phase, the proposed scheme constructs a virtual grid structure for merging the source nodes. Then, the proposed scheme aggregates the merging points from an originated merging point as the second phase. Simulation results show that the proposed scheme is superior to other schemes in terms of energy consumption.

EERS: Energy-Efficient Reference Node Selection Algorithm for Synchronization in Industrial Wireless Sensor Networks

Time synchronization is an essential issue in industrial wireless sensor networks (IWSNs). It assists perfect coordinated communications among the sensor nodes to preserve battery power. Generally, time synchronization in IWSNs has two major aspects of energy consumption and accuracy. In the literature, the energy consumption has not received much attention in contrast to the accuracy. In this paper, focusing on the energy consumption aspect, we introduce an energy-efficient reference node selection (EERS) algorithm for time synchronization in IWSNs. It selects and schedules a minimal sequence of connected reference nodes that are responsible for spreading timing messages. EERS achieves energy consumption synchronization by reducing the number of transmitted messages among the sensor nodes. To evaluate the performance of EERS, we conducted extensive experiments with Arduino Nano RF sensors and revealed that EERS achieves considerably fewer messages than previous techniques, robust time synchronization (R-Sync), fast scheduling and accurate drift compensation for time synchronization (FADS), and low power scheduling for time synchronization protocols (LPSS). In addition, simulation results for a large sensor network of 450 nodes demonstrate that EERS reduces the whole number of transmitted messages by 52%, 30%, and 13% compared to R-Sync, FADS, and LPSS, respectively.

Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks With Backward Residual Connections, Stochastic Softmax, and Hybridization

Spiking Neural Networks (SNNs) may offer an energy-efficient alternative for implementing deep learning applications. In recent years, there have been several proposals focused on supervised (conversion, spike-based gradient descent) and unsupervised (spike timing dependent plasticity) training methods to improve the accuracy of SNNs on large-scale tasks. However, each of these methods suffer from scalability, latency, and accuracy limitations. In this paper, we propose novel algorithmic techniques of modifying the SNN configuration with backward residual connections, stochastic softmax, and hybrid artificial-and-spiking neuronal activations to improve the learning ability of the training methodologies to yield competitive accuracy, while, yielding large efficiency gains over their artificial counterparts. Note, artificial counterparts refer to conventional deep learning/artificial neural networks. Our techniques apply to VGG/Residual architectures, and are compatible with all forms of training methodologies. Our analysis reveals that the proposed solutions yield near state-of-the-art accuracy with significant energy-efficiency and reduced parameter overhead translating to hardware improvements on complex visual recognition tasks, such as, CIFAR10, Imagenet datatsets.

A Two-Layer, Energy-Efficient Approach for Joint Power Control and Uplink-Downlink Channel Allocation in D2D Communication

Energy efficiency (EE) is a critical performance indicator for the device-to-device (D2D) communication underlaying cellular networks due to limited battery capacity and serious interference between user equipment. In this study, we proposed a power control and channel allocation scheme for the EE maximization of the D2D pairs, while jointly reusing uplink-downlink resources and guaranteeing the cellular users' (CUs) quality of service (QoS). The formulated problem was a mixed-integer nonlinear programming (MINLP) problem, which is generally an unsolved non-deterministic polynomial-time hardness (NP-hard) problem within polynomial time. To make it tractable to solve, the original problem was divided into two sub-problems: power control and channel allocation. A power control algorithm based on the Lambert W function was proposed to maximize the EE of the individual D2D pair. Assigning either an uplink or downlink resource to reuse, the EE of each D2D pair was calculated using the power control results. A channel allocation scheme based on the Kuhn-Munkres algorithm utilized the EE weights to optimize the overall EE of the D2D pairs. The simulation results verified the theoretical analysis and proved that the proposed algorithm could remarkably improve the EE of D2D pairs while guaranteeing the QoS of the CUs.

SalvageDNN: salvaging deep neural network accelerators with permanent faults through saliency-driven fault-aware mapping

Deep neural networks (DNNs) have proliferated in most of the application domains that involve data processing, predictive analysis and knowledge inference. Alongside the need for developing highly performance-efficient DNN accelerators, there is an utmost need to improve the yield of the manufacturing process in order to reduce the per unit cost of the DNN accelerators. To this end, we present 'SalvageDNN', a methodology to enable reliable execution of DNNs on the hardware accelerators with permanent faults (typically due to imperfect manufacturing processes). It employs a fault-aware mapping of different parts of a given DNN on the hardware accelerator (subjected to faults) by leveraging the saliency of the DNN parameters and the fault map of the underlying processing hardware. We also present novel modifications in a systolic array design to further improve the yield of the accelerators while ensuring reliable DNN execution using 'SalvageDNN' and negligible overheads in terms of area, power/energy and performance. This article is part of the theme issue 'Harmonizing energy-autonomous computing and intelligence'.

From the Sensor to the Cloud: Intelligence Partitioning for Smart Camera Applications

The Internet of Things has grown quickly in the last few years, with a variety of sensing, processing and storage devices interconnected, resulting in high data traffic. While some sensors such as temperature, or humidity sensors produce a few bits of data periodically, imaging sensors output data in the range of megabytes every second. This raises a complexity for battery operated smart cameras, as they would be required to perform intensive image processing operations on large volumes of data, within energy consumption constraints. By using intelligence partitioning we analyse the effects of different partitioning scenarios for the processing tasks between the smart camera node, the fog computing layer and cloud computing, in the node energy consumption as well as the real time performance of the WVSN (Wireless Vision Sensor Node). The results obtained show that traditional design space exploration approaches are inefficient for WVSN, while intelligence partitioning enhances the energy consumption performance of the smart camera node and meets the timing constraints.

Greener, Energy-Efficient and Sustainable Networks: State-Of-The-Art and New Trends

Although information and communications technologies (ICTs) have the potential of enabling powerful social, economic and environmental benefits, ICT systems give a non-negligible contribution to world electricity consumption and carbon dioxide (CO2) footprint. This contribution will sustain since the increased demand for user's connectivity and an explosion of traffic volumes necessitate continuous expansion of current ICTs services and deployment of new infrastructures and technologies which must ensure the expected user experiences and performance. In this paper, analyses of costs for the global annual energy consumption of telecommunication networks, estimation of ICT sector CO2 footprint contribution and predictions of energy consumption of all connected user-related devices and equipment in the period 2011-2030 are presented. Since presented estimations of network energy consumption trends for main communication sectors by 2030 shows that highest contribution to global energy consumption will come from wireless access networks and data centres (DCs), the rest of the paper analyses technologies and concepts which can contribute to the energy-efficiency improvements of these two sectors. More specifically, different paradigms for wireless access networks such as millimetre-wave communications, Long-Term Evolution in unlicensed spectrum, ultra-dense heterogeneous networks, device-to-device communications and massive multiple-input multiple-output communications have been analysed as possible technologies for improvement of wireless networks energy efficiency. Additionally, approaches related to the DC resource management, DCs power management, green DC monitoring and thermal management in DCs have been discussed as promising approaches to improvement of DC power usage efficiency. For each of analysed technologies, future research challenges and open issues have been summarised and discussed. Lastly, an overview of the accepted papers in the Special Issue dedicated to the green, energy-efficient and sustainable networks is presented.

VN-NDP: A Neighbor Discovery Protocol Based on Virtual Nodes in Mobile WSNs

As an indispensable part of Internet of Things (IoT), wireless sensor networks (WSNs) are more and more widely used with the rapid development of IoT. The neighbor discovery protocols are the premise of communication between nodes and networking in energy-limited self-organizing wireless networks, and play an important role in WSNs. Because the node energy is limited, neighbor discovery must operate in an energy-efficient manner, that is, under the condition of a given energy budget, the neighbor discovery performance should be as good as possible, such that the discovery latency would be as small as possible and the discovered neighbor percentage as large as possible. The indirect neighbor discovery mainly uses the information of the neighbors that have been found by a pairwise discovery method to more efficiently make a re-planning of the discovery wake-up schedules of the original pairwise neighbor discovery, thereby improving the discovery energy efficiency. The current indirect neighbor discovery methods are mainly divided into two categories: one involves removing the inefficient active slots in the original discovery wake-up schedules, and the other involves adding some efficient active slots. However, the two categories of methods have their own limitations. The former does not consider that this removal operation destroys the integrity of the original discovery wake-up schedules and hence the possibility of discovering new neighbors is reduced, which adversely affects the discovered neighbor percentage. For the latter category, there are still inefficient active slots that were not removed in the re-planned wake-up schedules. The motivation of this paper is to combine the advantages of these two types of indirect neighbor discovery methods, that is, to combine the addition of efficient active slots and the removal of inefficient active slots. To achieve this goal, this paper proposes, for the first time, the concept of virtual nodes in neighbor discovery to maximize the integrity of the original wake-up schedules and achieve the goals of adding efficient active slots and removing inefficient active slots. Specifically, a virtual node is a collaborative group that is formed by nodes within a small range. The nodes in a collaborative group share responsibility for the activating task of one member node, and the combination of these nodes’ wake-up schedules forms the full wake-up schedule of a node that only uses a pairwise method. In addition, this paper proposes a set of efficient group management mechanisms, and the key steps affecting energy efficiency are analyzed theoretically to obtain the energy-optimal parameters. The extended simulation experiments in multiple scenarios show that, compared with other methods, our neighbor discovery protocol based on virtual nodes (VN-NDP) has a significant improvement in average discovery delay and discovered neighbor percentage performance at a given energy budget. Compared with the typical indirect neighbor discovery algorithm EQS, a neighbor discovery with extended quorum system, our proposed VN-NDP method reduces the average discovery delay by up to 10.03% and increases the discovered neighbor percentage by up to 18.35%.

A Survey on Recent Trends and Open Issues in Energy Efficiency of 5G

The rapidly increasing interest from various verticals for the upcoming 5th generation (5G) networks expect the network to support higher data rates and have an improved quality of service. This demand has been met so far by employing sophisticated transmission techniques including massive Multiple Input Multiple Output (MIMO), millimeter wave (mmWave) bands as well as bringing the computational power closer to the users via advanced baseband processing units at the base stations. Future evolution of the networks has also been assumed to open many new business horizons for the operators and the need of not only a resource efficient but also an energy efficient ecosystem has greatly been felt. The deployment of small cells has been envisioned as a promising answer for handling the massive heterogeneous traffic, but the adverse economic and environmental impacts cannot be neglected. Given that 10% of the world’s energy consumption is due to the Information and Communications Technology (ICT) industry, energy-efficiency has thus become one of the key performance indicators (KPI). Various avenues of optimization, game theory and machine learning have been investigated for enhancing power allocation for downlink and uplink channels, as well as other energy consumption/saving approaches. This paper surveys the recent works that address energy efficiency of the radio access as well as the core of wireless networks, and outlines related challenges and open issues.

Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs

In wireless sensor networks, clustering routing algorithms have been widely used owing to their high energy-efficiency and scalability. In clustering schemes, the nodes are organized in the form of clusters, and each cluster is governed by a cluster head. Once the cluster heads are selected, they form a backbone network to periodically collect, aggregate, and forward data to the base station using minimum energy (cost) routing. This approach significantly improves the network lifetime. Therefore, a new cluster head selection method that uses a weighted sum method to calculate the weight of each node in the cluster and compare it with the standard weight of that particular cluster is proposed in this paper. The node with a weight closest to the standard cluster weight becomes the cluster head. This technique balances the load distribution and selects the nodes with highest residual energy in the network. Additionally, a data routing scheme is proposed to determine an energy-efficient path from the source to the destination node. This algorithm assigns a weight function to each link on the basis of a fuzzy membership function and intra-cluster communication cost within a cluster. As a result, a minimum weight path is selected using Dijkstra’s algorithm that improves the energy efficiency of the overall system. The experimental results show that the proposed algorithm shows better performance than some existing representative methods in the aspects of energy consumption, network lifetime, and system throughput.

Decoupling Office Energy Efficiency From Employees' Well-Being and Performance: A Systematic Review

Energy efficiency (i.e., the ratio of output of performance to input of energy) in office buildings can reduce energy costs and CO₂ emissions, but there are barriers to widespread adoption of energy efficient solutions in offices because they are often perceived as a potential threat to perceived comfort, well-being, and performance of office users. However, the links between offices' energy efficiency and users' performance and well-being through their moderators are neither necessary nor empirically confirmed. The purpose of this study is to carry out a systematic review to identify the existing empirical evidence regarding the relationships between energy-efficient solutions in sustainable office buildings and the perceptions of employees' productivity and well-being. Additionally, we aim to identify relevant boundary conditions for these relationships to occur. A systematic literature search of online databases for energy efficiency literature (e.g., Environment Complete, GreenFILE), employee literature (e.g., PsycINFO, Business Source Complete) and general social science literature (e.g., Academic Search Complete) yielded 34 empirical studies. Also, inclusion and exclusion criteria were set. The results suggest that it is possible to decouple energy costs from organizational outcomes such as employee well-being and performance. Also, they indicate the existence of moderators and mediators in the relationship between green office building solutions and well-being/performance. Directions for future research and the implications for practice considering different stakeholders interested in implementing green building solutions, adopting energy-saving measures in offices, and improving employees' functioning are suggested.

A Survey of Energy-Efficient Communication Protocols with QoS Guarantees in Wireless Multimedia Sensor Networks

In recent years, wireless multimedia sensor networks (WMSNs) have emerged as a prominent technique for delivering multimedia information such as still images and videos. Being under the great spotlight of research communities, however, multimedia delivery over resourceconstraint WMSNs poses great challenges, especially in terms of energy efficiency and quality-ofservice (QoS) guarantees. In this paper, recent developments in techniques for designing highly energy-efficient and QoS-capable WMSNs are surveyed. We first study the unique characteristicsand the relevantly imposed requirements of WMSNs. For each requirement we also summarize their existing solutions. Then we review recent research efforts on energy-efficient and QoS-awarecommunication protocols, including MAC protocols, with a focus on their prioritization and service differentiation mechanisms and disjoint multipath routing protocols.

Cooperative UAV Scheme for Enhancing Video Transmission and Global Network Energy Efficiency

Collaboration between multiple Unmanned Aerial Vehicles (UAVs) to set up a Flying Ad Hoc Network (FANET) is a growing trend since future applications claim for more autonomous and rapid deployable systems. The user experience on watching videos transmitted over FANETs should always be satisfactory even under influence of topology changes caused by the energy consumption of UAVs. In addition, the FANET must keep the UAVs cooperating as much as possible during a mission. However, one of the main challenges in FANET is how to mitigate the impact of limited energy resources of UAVs on the FANET operation in order to monitor the environment for a long period of time. In this sense, UAV replacement is required in order to avoid the premature death of nodes, network disconnections, route failures, void areas, and low-quality video transmissions. In addition, decision-making must take into account energy consumption associated with UAV movements, since they are generally quite energy-intensive. This article proposes a cooperative UAV scheme for enhancing video transmission and global energy efficiency called VOEI. The main goal of VOEI is to maintain the video with QoE support while supporting the nodes with a good connectivity quality level and flying for a long period of time. Based on an Software Defined Network (SDN) paradigm, the VOEI assumes the existence of a centrailized controller node to compute reliable and energy-efficiency routes, as well as detects the appropriate moment for UAV replacement by considering global FANET context information to provide energy-efficiency operations. Based on simulation results, we conclude that VOEI can effectively mitigate the energy challenges of FANET, since it provides energy-efficiency operations, avoiding network death, route failure, and void area, as well as network partitioning compared to state-of-the-art algorithm. In addition, VOEI delivers videos with suitable Quality of Experience (QoE) to end-users at any time, which is not achieved by the state-of-the-art algorithm.

A Topology Control with Energy Balance in Underwater Wireless Sensor Networks for IoT-Based Application

As part of the IoT-based application, underwater wireless sensor networks (UWSN), which are typically self-organized heterogeneous wireless network, are one of the research hot-spots using various sensors in marine exploration and water environment monitoring application fields, recently. Due to the serious attenuation of radio in water, acoustic or hybrid communication is a usual way for transmitting information among nodes, which dissipates much more energy to prevent the network failure and guarantee the quality of service (QoS). To address this issue, a topology control with energy balance, namely TCEB, is proposed for UWSN to overcome time-delay and other interference, as well as make the entire network load balance. With the given underwater network model and its specialized energy consumption model, we introduce the non-cooperative-game-based scheme to select the nodes with better performance as the cluster-heads. Afterwards, the intra-cluster and inter-cluster topology construction are, respectively, to form the effective communication links of the intra-cluster and inter-cluster, which aim to build energy-efficient topology to reduce energy consumption. With the demonstration of the simulation, the results show the proposed TCEB has better performance on energy-efficiency and throughput than three other representative algorithms in complex underwater environments.

On Maximizing the Throughput of Packet Transmission under Energy Constraints

More and more Internet of Things (IoT) wireless devices have been providing ubiquitous services over the recent years. Since most of these devices are powered by batteries, a fundamental trade-off to be addressed is the depleted energy and the achieved data throughput in wireless data transmission. By exploiting the rate-adaptive capacities of wireless devices, most existing works on energy-efficient data transmission try to design rate-adaptive transmission policies to maximize the amount of transmitted data bits under the energy constraints of devices. Such solutions, however, cannot apply to scenarios where data packets have respective deadlines and only integrally transmitted data packets contribute. Thus, this paper introduces a notion of weighted throughput, which measures how much total value of data packets are successfully and integrally transmitted before their own deadlines. By designing efficient rate-adaptive transmission policies, this paper aims to make the best use of the energy and maximize the weighted throughput. What is more challenging but with practical significance, we consider the fading effect of wireless channels in both offline and online scenarios. In the offline scenario, we develop an optimal algorithm that computes the optimal solution in pseudo-polynomial time, which is the best possible solution as the problem undertaken is NP-hard. In the online scenario, we propose an efficient heuristic algorithm based on optimal properties derived for the optimal offline solution. Simulation results validate the efficiency of the proposed algorithm.

An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications

Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone’s life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments. The developed chip collects real-time ECG data with two adopted channels for continuous monitoring of human heart activity. Then, these two channels and the AFE are built into a right leg drive right leg drive (RLD) driver circuit with lead-off detection and medical graded test signal. Human ECG data was collected at 60 beats per minute (BPM) to 120 BPM with 60 Hz noise and considered throughout the experimental set-up. Moreover, notch filter (cutoff frequency 60 Hz), high-pass filter (cutoff frequency 0.67 Hz), and low-pass filter (cutoff frequency 100 Hz) with cut-off frequencies of 60 Hz, 0.67 Hz, and 100 Hz, respectively, were designed with bilinear transformation for rectifying the power-line noise and artifacts while extracting real-time ECG signals. Finally, a transmission power control-based energy-efficient (ETPC) algorithm is proposed, implemented on the hardware and then compared with the several conventional TPC methods. Experimental results reveal that our developed chip collects real-time ECG data efficiently, and the proposed ETPC algorithm achieves higher energy savings of 35.5% with a slightly larger packet loss ratio (PLR) as compared to conventional TPC (e.g., constant TPC, Gao’s, and Xiao’s methods).

Mixed H₂/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H₂/H_∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance.

Optimal Rate Schedules with Data Sharing in Energy Harvesting Communication Systems

Despite the abundant research on energy-efficient rate scheduling polices in energy harvesting communication systems, few works have exploited data sharing among multiple applications to further enhance the energy utilization efficiency, considering that the harvested energy from environments is limited and unstable. In this paper, to overcome the energy shortage of wireless devices at transmitting data to a platform running multiple applications/requesters, we design rate scheduling policies to respond to data requests as soon as possible by encouraging data sharing among data requests and reducing the redundancy. We formulate the problem as a transmission completion time minimization problem under constraints of dynamical data requests and energy arrivals. We develop offline and online algorithms to solve this problem. For the offline setting, we discover the relationship between two problems: the completion time minimization problem and the energy consumption minimization problem with a given completion time. We first derive the optimal algorithm for the min-energy problem and then adopt it as a building block to compute the optimal solution for the min-completion-time problem. For the online setting without future information, we develop an event-driven online algorithm to complete the transmission as soon as possible. Simulation results validate the efficiency of the proposed algorithm.

Tunable and Lightweight On-Chip Event Detection for Implantable Bladder Pressure Monitoring Devices

Lower urinary tract dysfunctions, such as urinary incontinence and overactive bladder, are conditions that greatly affect the quality of life for millions of individuals worldwide. For those with more complex pathophysiologies, diagnosis of these conditions often requires a urodynamics study, providing physicians with a snapshot view of bladder mechanics. Recent advancements in implantable bladder pressure monitors and advanced data analysis techniques have made diagnosis through chronic monitoring a promising prospect. However, implants targeted at treatment must remain in the bladder for long periods of time, making minimizing power consumption a primary design objective. Currently, much of the typical implant's power draw is due to data transmission. Previous work has demonstrated an adaptive rate transmission technique to reduce power consumption. However, the ultimate reduction in power consumption can only be attained when the device does not transmit bladder pressure samples, but rather bladder events. In this paper, we present an algorithm and circuit level implementation for on-chip bladder pressure data compression and event detection. It is designed to be a complete, tunable, and lightweight diagnosis and treatment framework for bladder pressure monitoring implants, capable of selectively transmitting compressed bladder pressure data with tunable quality, "snapshots" of significant bladder events, or simply indicate events occurred for the highest energy efficiency. The design aims to minimize area through resource reuse, leading to a total area of 1.75 , and employs advanced VLSI techniques for power reduction. With compression and event detection enabled, the design consumes roughly 2.6 nW in TSMC technology. With only event detection, this reduces to 2.1 nW, making this approach ideal for long-life implantable bladder pressure monitoring devices.

Energy-Efficient Hosting Rich Content from Mobile Platforms with Relative Proximity Sensing

In this paper, we present a tiny networked mobile platform, termed Tiny-Web-Thing (T-Wing), which allows the sharing of data-intensive content among objects in cyber physical systems. The object includes mobile platforms like a smartphone, and Internet of Things (IoT) platforms for Human-to-Human (H2H), Human-to-Machine (H2M), Machine-to-Human (M2H), and Machine-to-Machine (M2M) communications. T-Wing makes it possible to host rich web content directly on their objects, which nearby objects can access instantaneously. Using a new mechanism that allows the Wi-Fi interface of the object to be turned on purely on-demand, T-Wing achieves very high energy efficiency. We have implemented T-Wing on an embedded board, and present evaluation results from our testbed. From the evaluation result of T-Wing, we compare our system against alternative approaches to implement this functionality using only the cellular or Wi-Fi (but not both), and show that in typical usage, T-Wing consumes less than 15× the energy and is faster by an order of magnitude.

An Artificial Measurements-Based Adaptive Filter for Energy-Efficient Target Tracking via Underwater Wireless Sensor Networks

We study the problem of energy-efficient target tracking in underwater wireless sensor networks (UWSNs). Since sensors of UWSNs are battery-powered, it is impracticable to replace the batteries when exhausted. This means that the battery life affects the lifetime of the whole network. In order to extend the network lifetime, it is worth reducing the energy consumption on the premise of sufficient tracking accuracy. This paper proposes an energy-efficient filter that implements the tradeoff between communication cost and tracking accuracy. Under the distributed fusion framework, local sensors should not send their weak information to the fusion center if their measurement residuals are smaller than the pre-given threshold. In order to guarantee the target tracking accuracy, artificial measurements are generated to compensate for those unsent real measurements. Then, an adaptive scheme is derived to take full advantages of the artificial measurements-based filter in terms of energy-efficiency. Furthermore, a computationally efficient optimal sensor selection scheme is proposed to improve tracking accuracy on the premise of employing the same number of sensors. Simulation demonstrates that our scheme has superior advantages in the tradeoff between communication cost and tracking accuracy. It saves much energy while loosing little tracking accuracy or improves tracking performance with less additional energy cost.

An Energy-Efficient Game-Theory-Based Spectrum Decision Scheme for Cognitive Radio Sensor Networks

A cognitive radio sensor network (CRSN) is a wireless sensor network in which sensor nodes are equipped with cognitive radio. In this paper, we propose an energy-efficient game-theory-based spectrum decision (EGSD) scheme for CRSNs to prolong the network lifetime. Note that energy efficiency is the most important design consideration in CRSNs because it determines the network lifetime. The central part of the EGSD scheme consists of two spectrum selection algorithms: random selection and game-theory-based selection. The EGSD scheme also includes a clustering algorithm, spectrum characterization with a Markov chain, and cluster member coordination. Our performance study shows that EGSD outperforms the existing popular framework in terms of network lifetime and coordination overhead.

Modular Energy-Efficient and Robust Paradigms for a Disaster-Recovery Process over Wireless Sensor Networks

Robust paradigms are a necessity, particularly for emerging wireless sensor network (WSN) applications. The lack of robust and efficient paradigms causes a reduction in the provision of quality of service (QoS) and additional energy consumption. In this paper, we introduce modular energy-efficient and robust paradigms that involve two archetypes: (1) the operational medium access control (O-MAC) hybrid protocol and (2) the pheromone termite (PT) model. The O-MAC protocol controls overhearing and congestion and increases the throughput, reduces the latency and extends the network lifetime. O-MAC uses an optimized data frame format that reduces the channel access time and provides faster data delivery over the medium. Furthermore, O-MAC uses a novel randomization function that avoids channel collisions. The PT model provides robust routing for single and multiple links and includes two new significant features: (1) determining the packet generation rate to avoid congestion and (2) pheromone sensitivity to determine the link capacity prior to sending the packets on each link. The state-of-the-art research in this work is based on improving both the QoS and energy efficiency. To determine the strength of O-MAC with the PT model; we have generated and simulated a disaster recovery scenario using a network simulator (ns-3.10) that monitors the activities of disaster recovery staff; hospital staff and disaster victims brought into the hospital. Moreover; the proposed paradigm can be used for general purpose applications. Finally; the QoS metrics of the O-MAC and PT paradigms are evaluated and compared with other known hybrid protocols involving the MAC and routing features. The simulation results indicate that O-MAC with PT produced better outcomes.

A routing protocol based on energy and link quality for Internet of Things applications

The Internet of Things (IoT) is attracting considerable attention from the universities, industries, citizens and governments for applications, such as healthcare, environmental monitoring and smart buildings. IoT enables network connectivity between smart devices at all times, everywhere, and about everything. In this context, Wireless Sensor Networks (WSNs) play an important role in increasing the ubiquity of networks with smart devices that are low-cost and easy to deploy. However, sensor nodes are restricted in terms of energy, processing and memory. Additionally, low-power radios are very sensitive to noise, interference and multipath distortions. In this context, this article proposes a routing protocol based on Routing by Energy and Link quality (REL) for IoT applications. To increase reliability and energy-efficiency, REL selects routes on the basis of a proposed end-to-end link quality estimator mechanism, residual energy and hop count. Furthermore, REL proposes an event-driven mechanism to provide load balancing and avoid the premature energy depletion of nodes/networks. Performance evaluations were carried out using simulation and testbed experiments to show the impact and benefits of REL in small and large-scale networks. The results show that REL increases the network lifetime and services availability, as well as the quality of service of IoT applications. It also provides an even distribution of scarce network resources and reduces the packet loss rate, compared with the performance of well-known protocols.

Cluster Size Optimization in Sensor Networks with Decentralized Cluster-Based Protocols

Network lifetime and energy-efficiency are viewed as the dominating considerations in designing cluster-based communication protocols for wireless sensor networks. This paper analytically provides the optimal cluster size that minimizes the total energy expenditure in such networks, where all sensors communicate data through their elected cluster heads to the base station in a decentralized fashion. LEACH, LEACH-Coverage, and DBS comprise three cluster-based protocols investigated in this paper that do not require any centralized support from a certain node. The analytical outcomes are given in the form of closed-form expressions for various widely-used network configurations. Extensive simulations on different networks are used to confirm the expectations based on the analytical results. To obtain a thorough understanding of the results, cluster number variability problem is identified and inspected from the energy consumption point of view.

Energy-efficiency analysis of a distributed queuing medium access control protocol for biomedical wireless sensor networks in saturation conditions

The aging population and the high quality of life expectations in our society lead to the need of more efficient and affordable healthcare solutions. For this reason, this paper aims for the optimization of Medium Access Control (MAC) protocols for biomedical wireless sensor networks or wireless Body Sensor Networks (BSNs). The hereby presented schemes always have in mind the efficient management of channel resources and the overall minimization of sensors' energy consumption in order to prolong sensors' battery life. The fact that the IEEE 802.15.4 MAC does not fully satisfy BSN requirements highlights the need for the design of new scalable MAC solutions, which guarantee low-power consumption to the maximum number of body sensors in high density areas (i.e., in saturation conditions). In order to emphasize IEEE 802.15.4 MAC limitations, this article presents a detailed overview of this de facto standard for Wireless Sensor Networks (WSNs), which serves as a link for the introduction and initial description of our here proposed Distributed Queuing (DQ) MAC protocol for BSN scenarios. Within this framework, an extensive DQ MAC energy-consumption analysis in saturation conditions is presented to be able to evaluate its performance in relation to IEEE 802.5.4 MAC in highly dense BSNs. The obtained results show that the proposed scheme outperforms IEEE 802.15.4 MAC in average energy consumption per information bit, thus providing a better overall performance that scales appropriately to BSNs under high traffic conditions. These benefits are obtained by eliminating back-off periods and collisions in data packet transmissions, while minimizing the control overhead.

DRDT: distributed and reliable data transmission with cooperative nodes for lossy wireless sensor networks

Recent studies have shown that in realistic wireless sensor network environments links are extremely unreliable. To recover from corrupted packets, most routing schemes with an assumption of ideal radio environments use a retransmission mechanism, which may cause unnecessary retransmissions. Therefore, guaranteeing energy-efficient reliable data transmission is a fundamental routing issue in wireless sensor networks. However, it is not encouraged to propose a new reliable routing scheme in the sense that every existing routing scheme cannot be replaced with the new one. This paper proposes a Distributed and Reliable Data Transmission (DRDT) scheme with a goal to efficiently guarantee reliable data transmission. In particular, this is based on a pluggable modular approach so that it can be extended to existing routing schemes. DRDT offers reliable data transmission using neighbor nodes, i.e., helper nodes. A helper node is selected among the neighbor nodes of the receiver node which overhear the data packet in a distributed manner. DRDT effectively reduces the number of retransmissions by delegating the retransmission task from the sender node to the helper node that has higher link quality to the receiver node when the data packet reception fails due to the low link quality between the sender and the receiver nodes. Comprehensive simulation results show that DRDT improves end-to-end transmission cost by up to about 45% and reduces its delay by about 40% compared to existing schemes.