Embedded Sensor Nodes Research Articles

Development of Wireless Sensor Network for Environment Monitoring and Its Implementation Using SSAIL Technology.

The Internet of Things (IoT) technology and its applications are turning real-world things into smart objects, integrating everything under a common infrastructure to manage performance through a software application and offering upgrades with integrated web servers in a timely manner. Quality of life, the green economy, and pollution management in society require comprehensive environmental monitoring systems with easy-to-use features and maintenance. This research suggests implementing a wireless sensor network with embedded sensor nodes manufactured using the Selective Surface Activation Induced by Laser technology. Such technology allows the integration of electrical circuits with free-form plastic sensor housing. In this work, a low-cost asynchronous web server for monitoring temperature and humidity sensors connected to the ESP32 Wi-Fi module has been developed. Data from sensor nodes across the facility are collected and displayed in real-time charts on a web server. Multiple web clients on the same network can access the sensor data. The energy to the sensor nodes could be powered by harvesting energy from surrounding sources of electromagnetic radiation. This automated and self-powered system monitors environmental and climatic factors, helps with timely action, and benefits sensor design by allowing antenna and rf-circuit formation on various plastics, even on the body of the device itself. It also provides greater flexibility in hardware modification and rapid large-scale deployment.

Acoustic vibrations for versatile wireless power transfer

We present an overview of prior/recent work done at the US Army Combat Capabilities Command—Army Research Laboratory on using acoustic waves for wireless power transfer (WPT). In the past we have demonstrated through-air ultrasonic phased array power beaming, through-metal longer distance transfer using Lamb waves, and power/data transfer to embedded sensor nodes. Acoustic WPT has the advantage of not being shielded by metal, and can achieve higher efficiencies than inductive WPT at larger distances. Part of our recent work is on developing a through-metal acoustic WPT based wireless UAV recharging platform. For the first time, we have demonstrated a self-detachable acoustic WPT receiver, using the switchable magnetic force from electro-permanent-magnets to attach a receiver transducer onto a steel platform to receive power (a transmitter is bonded on the other side of the platform), without the need of any liquid couplant. The magnetic force enhances the mechanical mode coupling, while allowing the receiver to attach/detach freely from the platform. AC-AC power efficiency up to 63% was achieved, and with the appropriate power electronics and matching network, an overall DC-DC efficiency of 44% was achieved when charging a 3-cell LiPo UAV battery with 4.6 W, with higher power levels achievable.

Designing a Power Efficient Sensor Node Microcontroller

The energy efficiency of wireless sensor nodes is determined by the power efficiency of underlying hardware, network topology and the profile of target application. In this paper, we present the methodology aimed to select optimal power saving strategy when designing an embedded sensor node microcontroller for specific application. The goal of presented methodology is to select the best-suited power saving techniques to be implemented in sensor node hardware based on the activity profile of the target application. The paper describes applied approach and shows the results of the microcontroller implementation targeting security demanding sensor network applications.

WSN Routing Protocol Using a Multiobjective Greedy Approach

Due to the widespread use of communication networks and the ease of transmitting and gathering information through these networks, wireless sensor networks (WSN) have become increasingly popular. Usability in any environment without the need for environmental monitoring and engineering of these networks has led to their increasing usage in various fields. Routing information from the sensor node to sink, so that node energy is consumed uniformly and network life is not reduced, is one of the most important challenges in wireless sensor networks. Most wireless networks have no infrastructure, and embedded sensor nodes have limited power. Thus, the early termination of the wireless node’s energy based on the transmission of messages over the network can disrupt the entire network process. In this paper, the object is designed to find the optimal path in WSN based on the multiobjective greedy approach to the near optimal path. The proposed model is presented in this method to transfer sensed data of the sensor network to the base station for the desired applications. In this method, the sensor nodes are identified as adjacent nodes based on their distance. The energy of all nodes initially is approximately equal, which decreases with the transfer of information between the nodes. In this way, when a node senses a message, it checks several factors for transmitting information to its adjacent nodes and selects the node with the largest amount of factors as the next hop. The simulation results show that the energy consumption in the network grids is almost symmetrically presented, and the network lifetime is reduced with a gentle slope that provides optimum energy consumption in the networks. Also, the packet transmission delay in the network reaches 450 milliseconds for the transmission of information between 15 nodes and 650 connections. Besides, network throughput increases by approximately 97%. It also shows better performance compared to other previous methods in terms of evaluation criteria.

Wirelessly Powered Embedded Sensor Nodes for Internal Structural Health Monitoring

Monitoring large civil structures during construction and over their entire lifetime is key to being able to predict structural weaknesses and provide predictive maintenance. Existing approaches to structural health monitoring typically rely on wired or wireless sensors placed on the exterior of the structure. Although these approaches, being easy to power and replace, provide useful information, they fall short of measuring what is happening deep within the structure itself, e.g. within foundations or within supporting beams. Wireless sensors embedded inside are particularly attractive for these applications, but lifetime is limited by the battery capacity. Charging these sensors has been the bottleneck. Existing wireless power transfer approaches based on electromagnetic propagation suffer heavy attenuation due to the high dielectric absorption of the dense concrete. To resolve the issue, the limitation of dielectric absorption is tackled with a novel physical layer. In this work, power transfer is built on the fact that nearly all concrete structures are reinforced with steel bars, to provide tensile strength. Instead of transferring power over a few centimetres of concrete with a large transmitter as previous work has attempted to do, the reinforcing steel can act as a conduit for extremely low-frequency magneto-inductive fields.

Towards a Secure Thermal-Energy Aware Routing Protocol in Wireless Body Area Network Based on Blockchain Technology.

The emergence of biomedical sensor devices, wireless communication, and innovation in other technologies for healthcare applications result in the evolution of a new area of research that is termed as Wireless Body Area Networks (WBANs). WBAN originates from Wireless Sensor Networks (WSNs), which are used for implementing many healthcare systems integrated with networks and wireless devices to ensure remote healthcare monitoring. WBAN is a network of wearable devices implanted in or on the human body. The main aim of WBAN is to collect the human vital signs/physiological data (like ECG, body temperature, EMG, glucose level, etc.) round-the-clock from patients that demand secure, optimal and efficient routing techniques. The efficient, secure, and reliable designing of routing protocol is a difficult task in WBAN due to its diverse characteristic and restraints, such as energy consumption and temperature-rise of implanted sensors. The two significant constraints, overheating of nodes and energy efficiency must be taken into account while designing a reliable blockchain-enabled WBAN routing protocol. The purpose of this study is to achieve stability and efficiency in the routing of WBAN through managing temperature and energy limitations. Moreover, the blockchain provides security, transparency, and lightweight solution for the interoperability of physiological data with other medical personnel in the healthcare ecosystem. In this research work, the blockchain-based Adaptive Thermal-/Energy-Aware Routing (ATEAR) protocol for WBAN is proposed. Temperature rise, energy consumption, and throughput are the evaluation metrics considered to analyze the performance of ATEAR for data transmission. In contrast, transaction throughput, latency, and resource utilization are used to investigate the outcome of the blockchain system. Hyperledger Caliper, a benchmarking tool, is used to evaluate the performance of the blockchain system in terms of CPU utilization, memory, and memory utilization. The results show that by preserving residual energy and avoiding overheated nodes as forwarders, high throughput is achieved with the ultimate increase of the network lifetime. Castalia, a simulation tool, is used to evaluate the performance of the proposed protocol, and its comparison is made with Multipath Ring Routing Protocol (MRRP), thermal-aware routing algorithm (TARA), and Shortest-Hop (SHR). Evaluation results illustrate that the proposed protocol performs significantly better in balancing of temperature (to avoid damaging heat effect on the body tissues) and energy consumption (to prevent the replacement of battery and to increase the embedded sensor node life) with efficient data transmission achieving a high throughput value.

Improved Knowledge Distillation via Teacher Assistant

Despite the fact that deep neural networks are powerful models and achieve appealing results on many tasks, they are too large to be deployed on edge devices like smartphones or embedded sensor nodes. There have been efforts to compress these networks, and a popular method is knowledge distillation, where a large (teacher) pre-trained network is used to train a smaller (student) network. However, in this paper, we show that the student network performance degrades when the gap between student and teacher is large. Given a fixed student network, one cannot employ an arbitrarily large teacher, or in other words, a teacher can effectively transfer its knowledge to students up to a certain size, not smaller. To alleviate this shortcoming, we introduce multi-step knowledge distillation, which employs an intermediate-sized network (teacher assistant) to bridge the gap between the student and the teacher. Moreover, we study the effect of teacher assistant size and extend the framework to multi-step distillation. Theoretical analysis and extensive experiments on CIFAR-10,100 and ImageNet datasets and on CNN and ResNet architectures substantiate the effectiveness of our proposed approach.

A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing

The hardware of networked embedded sensor nodes is in continuous evolution, from those 8-bit MCUs-based platforms such as Mica, up to powerful Edge nodes that even include custom hardware devices, such as FPGAs in the Cookies platform. This evolution process comes up with issues related to the deployment of the Internet of Things, particularly in terms of performance and communication bottlenecks. Moreover, the associated integration process from the Edge up to the Cloud layer opens new security concerns that are key to assure the end-to-end trustability and interoperability. This work tackles these questions by proposing a novel embedded Edge platform based on an EFR32 SoC from Silicon Labs with Contiki-NG OS that includes an ARM Cortex M4 MCU and an IEEE 802.15.4 transceiver, used for resource-constrained low-power communication capabilities. This IoT Edge node integrates security by hardware, adding support for confidentiality, integrity and availability, making this Edge node ultra-secure for most of the common attacks in wireless sensor networks. Part of this security relies on an energy-efficient hardware accelerator that handles identity authentication, session key creation and management. Furthermore, the modular hardware platform aims at providing reliability and robustness in low-power distributed sensing application contexts on what is called the Extreme Edge, and for that purpose a lightweight multi-hop routing strategy for supporting dynamic discovery and interaction among participant devices is fully presented. This embedded algorithm has served as the baseline end-to-end communication capability to validate the IoT hardware platform through intensive experimental tests in a real deployment scenario.

An Overview of Design Patterns for Self-Adaptive Systems in the Context of the Internet of Things

The Internet of Things (IoT) requires the integration of all available, highly specialized, and heterogeneous devices, ranging from embedded sensor nodes to servers in the cloud. The self-adaptive research domain provides adaptive capabilities that can support the integration in IoT systems. However, developing such systems is a challenging, error-prone, and time-consuming task. In this context, design patterns propose already used and optimized solutions to specific problems in various contexts. Applying design patterns might help to reuse existing knowledge about similar development issues. However, so far, there is a lack of taxonomies on design patterns for self-adaptive systems. To tackle this issue, in this paper, we provide a taxonomy on design patterns for self-adaptive systems that can be transferred to support adaptivity in IoT systems. Besides describing the taxonomy and the design patterns, we discuss their applicability in an Industrial IoT case study.

Lightweight Server-Assisted H-K Compression for Image-Based Embedded Wireless Sensor Network

Embedded wireless imaging systems based on wireless camera sensor network can be used to observe biological phenomena unobtrusively in various environmental monitoring applications. In our prior work, we have shown the feasibility and benefits of such a system through actual deployments. We have also shown that low-complexity data compression schemes can be employed to improve image transfer rate or to lower the energy costs of communication. In this paper, we extend upon our prior work and propose a scheme called as H–K compression , a simple lightweight image compression algorithm combining the ideas of Huffman coding and K-means clustering. Specifically, H–K compression applies K-means clustering for pixel color grouping and Huffman coding for the group color encoding, but combines the two schemes into one algorithm which can reduce the computational cost. Using 100 000 images collected from our pilot deployments at James Reserve, we study the applicability and impact of the proposed algorithm. Our results suggest that the cost of running the learning steps on an embedded sensor node may outweigh the benefit of compression, but offloading the learning can provide significant energy gains. Evaluations using the data set show that our proposed scheme compresses the data by $\sim$ 57% and reduces power usage by $\sim$ 43% when sending image updates from a bird nest periodically every 15 min.

A priority based energy harvesting scheme for charging embedded sensor nodes in wireless body area networks.

This research work proposes a novel priority aware schedule based charging algorithm that uses wireless power transfer (WPT) technique in order to charge embedded sensor nodes (SNs) in a wireless body area network (WBAN). Implanted sensor nodes in WBANs require energy for both information extraction and data transmission to the remote controller unit. Thus, energy shortage of these SNs deteriorates due to the data transmission process of the patient health monitoring system. However, continuous operation by means of electromagnetic induction for energy harvesting, obtained from ambient sources, reduces the overall efficiency of the primary unit. With this paradigm in sight, an algorithm demonstrating the modeling of a priority-based mechanism is proposed in order to ensure proper sensor voltage level and to reduce the transmission losses. Medium access control (MAC) protocols are used for inductive powering from the primary unit to the secondary unit in a collision-free centralized scheduling scheme. Therefore, the proposed wireless charging algorithm for implanted SNs in WBAN is designed as per carrier sense multiple access with collision avoidance (CSMA/CA) technique. Because of this, the overall power consumption of SNs for certain operation periods, successful charging probabilities for multiple SNs, and instantaneous power requirements are considered as key performance measures of analysis. It is assumed that proper energy storage in both transmitters and receivers can handle channel interference and traffic contention. Simulation results verify that a significant reduction in power consumption for the proposed priority aware algorithm will maintain almost similar output. For this reason, saturating class—C as well as class—E driver circuits have been used to justify the performance in two different circuit topologies. Effects of priority with respect to the full charge period have also been observed for the multi-node system. Furthermore, from performance analysis, it has been demonstrated that the scheduling scheme causes both single MOSFET composed saturating class—C and Lchoke modeled class—E associated driver circuits to be considerably more loss efficient than corresponding existing ones.

A cloud-assisted malware detection and suppression framework for wireless multimedia system in IoT based on dynamic differential game

As a novel dynamic network service infrastructure, Internet of Things (IoT) has gained remarkable popularity with obvious superiorities in the interoperability and real-time communication. Despite of the convenience in collecting information to provide the decision basis for the users, the vulnerability of embedded sensor nodes in multimedia devices makes the malware propagation a growing serious problem, which would harm the security of devices and their users financially and physically in wireless multimedia system (WMS). Therefore, many researches related to the malware propagation and suppression have been proposed to protect the topology and system security of wireless multimedia network. In these studies, the epidemic model is of great significance to the analysis of malware propagation. Considering the cloud and state transition of sensor nodes, a cloud-assisted model for malware detection and the dynamic differential game against malware propagation are proposed in this paper. Firstly, a SVM based malware detection model is constructed with the data sharing at the security platform in the cloud. Then the number of malware-infected nodes with physical infectivity to susceptible nodes is calculated precisely based on the attributes of WMS transmission. Then the state transition among WMS devices is defined by the modified epidemic model. Furthermore, a dynamic differential game and target cost function are successively derived for the Nash equilibrium between malware and WMS system. On this basis, a saddle-point malware detection and suppression algorithm is presented depending on the modified epidemic model and the computation of optimal strategies. Numerical results and comparisons show that the proposed algorithm can increase the utility of WMS efficiently and effectively.

Study of Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions

Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts across many areas of modern day living. This offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. The proliferation of these devices in a communicating-actuating network creates the Internet of Things (IoT), wherein, sensors and actuators blend seamlessly with the environment around us, and the information is shared across platforms in order to develop a common operating picture (COP). Fuelled by the recent adaptation of a variety of enabling wireless technologies such as RFID tags and embedded sensor and actuator nodes, the IoT has stepped out of its infancy and is the the next revolutionary technology in transforming the Internet into a fully integrated Future Internet. As we move from www (static pages web) to web2 (social networking web) to web3 (ubiquitous computing web), the need for data-on-demand using sophisticated intuitive queries increases significantly. This paper presents a Cloud centric vision for worldwide implementation of Internet of Things. The key enabling technologies and application domains that are likely to drive IoT research in the near future are discussed. A Cloud implementation using Aneka, which is based on interaction of private and public Clouds is presented. We conclude our IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.

Precomputation Methods for Hash-Based Signatures on Energy-Harvesting Platforms

Energy-harvesting techniques can be combined with wireless embedded sensors to obtain battery-free platforms with an extended lifetime. Although energy-harvesting offers a continuous supply of energy, the delivery rate is typically limited to a few Joules per day. This is a severe constraint to the achievable computing throughput on the embedded sensor node, and to the achievable latency obtained from applications running on those nodes. In this paper, we address these constraints with precomputation. The idea is to reduce the amount of computations required in response to application inputs, by partitioning the algorithm in an offline part, computed before the inputs are available, and an online part, computed in response to the actual input. We show that this technique works well on hash-based cryptographic signatures, which have a complex key generation for each new message that requires a signature. By precomputing the key-material, and by storing it as run-time coupons in non-volatile memory, there is a drastic reduction of the run-time energy needs for a signature, and a drastic reduction of the run-time latency to generate it. For a Winternitz hash-based scheme at 84-bit quantum security level on a MSP430 microcontroller, we measured a run-time energy reduction of 11.9 $\times$ and a run-time latency reduction of 23.5 $\times$ .

An Embedded Sensor Node Microcontroller with Crypto-Processors.

Wireless sensor network applications range from industrial automation and control, agricultural and environmental protection, to surveillance and medicine. In most applications, data are highly sensitive and must be protected from any type of attack and abuse. Security challenges in wireless sensor networks are mainly defined by the power and computing resources of sensor devices, memory size, quality of radio channels and susceptibility to physical capture. In this article, an embedded sensor node microcontroller designed to support sensor network applications with severe security demands is presented. It features a low power 16-bitprocessor core supported by a number of hardware accelerators designed to perform complex operations required by advanced crypto algorithms. The microcontroller integrates an embedded Flash and an 8-channel 12-bit analog-to-digital converter making it a good solution for low-power sensor nodes. The article discusses the most important security topics in wireless sensor networks and presents the architecture of the proposed hardware solution. Furthermore, it gives details on the chip implementation, verification and hardware evaluation. Finally, the chip power dissipation and performance figures are estimated and analyzed.

Monitoring algal blooms using active learning camera sensor networks

The frequent occurrence of toxin-producing algal blooms is a serious concern for the ecological status of inland water and for human and animal health. In this paper, camera sensor networks are used to monitor algal blooms in lake for the first time. A distributed machine learning based algal blooms recognition (DMLAR) algorithm is proposed and implemented on embedded sensor nodes. DMLAR accurately recognises algal blooms and estimates their amounts in real-time based on the pictures of water surface. The machine learning model used by DMLAR is constructed by designing an active learning task, which is accomplished by collaborative embedded sensor nodes in a distributed manner. Extensive field experiments with large dataset are performed on the system deployed in lake since May 2010. Experiment results show that DMLAR outperforms two widely used methods both in terms of estimation accuracy and execution time, using nearly the same memory and energy.

Multi-Core Embedded Wireless Sensor Networks: Architecture and Applications

Technological advancements in the silicon industry, as predicted by Moore's law, have enabled integration of billions of transistors on a single chip. To exploit this high transistor density for high performance, embedded systems are undergoing a transition from single-core to multi-core. Although a majority of embedded wireless sensor networks (EWSNs) consist of single-core embedded sensor nodes, multi-core embedded sensor nodes are envisioned to burgeon in selected application domains that require complex in-network processing of the sensed data. In this paper, we propose an architecture for heterogeneous hierarchical multi-core embedded wireless sensor networks (MCEWSNs) as well as an architecture for multi-core embedded sensor nodes used in MCEWSNs. We elaborate several compute-intensive tasks performed by sensor networks and application domains that would especially benefit from multi-core embedded sensor nodes. This paper also investigates the feasibility of two multi-core architectural paradigms-symmetric multiprocessors (SMPs) and tiled many-core architectures (TMAs)-for MCEWSNs. We compare and analyze the performance of an SMP (an Intel-based SMP) and a TMA (Tilera's TILEPro64) based on a parallelized information fusion application for various performance metrics (e.g., runtime, speedup, efficiency, cost, and performance per watt). Results reveal that TMAs exploit data locality effectively and are more suitable for MCEWSN applications that require integer manipulation of sensor data, such as information fusion, and have little or no communication between the parallelized tasks. To demonstrate the practical relevance of MCEWSNs, this paper also discusses several state-of-the-art multi-core embedded sensor node prototypes developed in academia and industry. We further discuss research challenges and future research directions for MCEWSNs.

Fast Parallel Implementation for Random Network Coding on Embedded Sensor Nodes

Network coding is becoming essential part of network systems since it enhances system performance in various ways. To take full advantage of network coding, however, it is vital to guarantee low latency in the decoding process and thus parallelization of random network coding has drawn broad attention from the network coding community. In this paper, we investigate the problem of parallelizing random network coding for embedded sensor systems with multicore processors. Recently, general purpose graphics processing unit (GPGPU) technology has paved the way for parallelizing random network coding; however, it is not an option on embedded sensor nodes without GPUs and thus it is indispensable to leverage multicore processors which are becoming more common in embedded sensor nodes. We propose a novel random network coding parallelization technique that can fully exploit multicore processors. In our experiments, our parallel method exhibits over 150% throughput enhancement compared to existing state-of-the-art implementations on an embedded system.

On the interaction of clocks, power, and synchronization in duty-cycled embedded sensor nodes

The efficiency of the time synchronization service in wireless sensor networks is tightly connected to the design of the radio, the quality of the clocking hardware, and the synchronization algorithm employed. While improvements can be made on all levels of the system, over the last few years most work has focused on the algorithmic level to minimize message exchange and in radio architectures to provide accurate time-stamping mechanisms. Surprisingly, the influences of the underlying clock system and its impact on the overall synchronization accuracy has largely been unstudied. In this work, we investigate the impact of the clocking subsystem on the time synchronization service and address, in particular, the influence of changes in environmental temperature on clock drift in highly duty-cycled wireless sensor nodes. We also develop formulas that help the system architect choose the optimal resynchronization period to achieve a given synchronization accuracy. We find that the synchronization accuracy has a two region behavior. In the first region, the synchronization accuracy is limited by quantization error, while int he second region changes in environmental temperature impact the achievable accuracy. We verify our analytic results in simulation and real hardware experiments.

Energy-driven distribution of signal processing applications across wireless sensor networks

Wireless sensor network (WSN) applications have been studied extensively in recent years. Such applications involve resource-limited embedded sensor nodes that have small size and low power requirements. Based on the need for extended network lifetimes in WSNs in terms of energy use, the energy efficiency of computation and communication operations in the sensor nodes becomes critical. Digital Signal Processing (DSP) applications typically require intensive data processing operations and as a result are difficult to implement directly in resource-limited WSNs. In this article, we present a novel design methodology for modeling and implementing computationally intensive DSP applications applied to wireless sensor networks. This methodology explores efficient modeling techniques for DSP applications, including data sensing and processing; derives formulations of Energy-Driven Partitioning (EDP) for distributing such applications across wireless sensor networks; and develops efficient heuristic algorithms for finding partitioning results that maximize the network lifetime. To address such an energy-driven partitioning problem, this article provides a new way of aggregating data and reducing communication traffic among nodes based on application analysis. By considering low data token delivery points and the distribution of computation in the application, our approach finds energy-efficient trade-offs between data communication and computation.