Sensor Battery Research Articles

A distributed coverage hole recovery approach based on reinforcement learning for Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), various anomalies may arise and reduce their reliability and efficiency. For example, Coverage Hole can occur in such networks due to several causes, such as damaging events, sensors battery exhaustion, hardware failure, and software bugs. Modern trends to use relocation of deployed sensor nodes when the manual addition of nodes is neither doable nor economical in many applications have attracted attention. The lack of central supervision and control in harsh and hostile environments have encouraged researchers to shift from centralized to distributed node relocation schemes. In this paper, a new game theory approach based on reinforcement learning to recover Coverage Holes in a distributed way is proposed. For the formulated potential game, sensor nodes can recover Coverage Holes using only local acquaintances. To reduce the coverage gaps, the combined action of node reposition and sensing range adjustment is chosen by each sensor node. The simulation results prove that, unlike previous methods, the proposed approach can sustain a network overall coverage in the presence of random damage events.

Analysis of energy consumption in a wireless sensor network using fuzzy memberships function

The use of sensors in every application as well as in today’s life has become a huge demand. Since that, the public sphere is starting to move to mobile applications that are easy to access. For example, child monitoring at school, health monitoring, tracking system, fire detection and so on. Thus, the Wireless Sensor Network (WSN) is the preferred choice to meet these needs. However, despite the enthusiasm of building various applications using sensors, the WSN itself is still hampered by limited battery usage. Due to some applications that require long battery life, various types of research have been conducted to address this problem. One of these is the involvement of artificial intelligent (AI) in extending the life of a battery on a sensor. Fuzzy Logic (FL) is one of the preferred AI that have been chosen by researchers to be implemented with WSN especially to protract the lifetime of WSN. In Fuzzy Logic, there are three membership functions that need to investigate the capability of it towards the WSN applications. The proposed approach is by combining different types of Fuzzy Logic membership functions which are Triangular with Gaussian, Gaussian with Trapezoidal and Trapezoidal with Triangular to get the best results for analysing the use of sensor batteries. The parameters involved for the cluster head selection are communication cost, centrality and residual energy as Fuzzy inputs. This approach will use an existing algorithm which is Multi-Tier Algorithm (MAP) and this is a part of the MAP enhancement towards the WSN lifetime. The results will compare, discuss and analyst the number of dead nodes and energy usage of the sensor node during data transmission. In conclusion, through this approach, it able to prolong the lifetime for the sensor network since the proposed technique can reduce the energy usage of the sensor nodes.

Optimization of sensor battery charging to maximize lifetime in a wireless sensors network

The maximum network lifetime is a well known and studied optimization problem. The aim is to appropriately schedule the activation intervals of the individual sensing devices composing a wireless sensor network used for monitoring purposes, in order to keep the network operational for the longest period of time (network lifetime). In this work, we extend this problem by taking into account the issue of charging the sensor batteries. More specifically, it has to be decided how much charge should be provided to each sensor, given the existence of a charging device with limited energy availability. An exact column generation algorithm embedding a genetic algorithm for the subproblem is proposed. Computational results reveal that by appropriately choosing the charge levels, remarkable network lifetime improvements can be obtained, in particular when the available energy is scarce.

Robust scheduling for target tracking using wireless sensor networks

A wireless sensor network (WSN) is a group of sensors deployed in an area, with all of them working on a battery and with direct communications inside the network. A fairly common situation, addressed in this work, is to monitor and record data with a WSN about vehicles (planes, terrestrial vehicles, boats, etc) passing by an area with damaged infrastructures. In such a context, an activation schedule for the sensors ensuring a continuous coverage of all the targets is required. Furthermore, the collected data, in order to be treated, have to be transmitted to a base station in the area, near the sensors. In this work, the future monitoring missions of the network are also taken into account, as well as the energy consumption of the current mission. We also consider that the spatial trajectories of the targets are known, whereas the speed of the targets along their trajectories are estimated, and subject to uncertainty. Hence, the main objective is to seek solutions that can withstand earliness and tardiness from the previsions. We propose a formulation of the problem with three different objectives and a solution method with experiments and results. The objectives are treated in a lexicographic order as follows (i) maximize the robustness schedule to cope with the advances and delaqui leys of the targets, (ii) maximize the minimum of monitoring time we can guarantee in priority areas, (iii) maximize the amount of energy left in the sensor batteries. We propose new upper bounds on the robustness measure, that are exploited by the solution approach whose complexity is shown to be pseudo-polynomial. The solution approach is based on a preprocessing step called discretisation, and the resolution of a series of linear programs.

A new software-defined network architecture to solve energy balance problems in wireless sensor networks

Wireless sensor networks (WSNs) are an important part of the internet of things. In WSNs, the sensor battery is limited, and the entire network has a data aggregation node called Sink. When the sensor transmits data to the Sink in a multi-hop manner, the sensors around the Sink also help other sensors to forward the data to the Sink. This causes those sensors to die faster than other sensors. This problem is called energy hole problem. Previous solutions such as sensor uneven distribution schemes and clustering schemes are not only difficult to implement, but also increase the design cost of the system. In particular, traditional WSNs cannot be updated once deployed. In order to solve the above problem, we propose a method of combining SDN and WSNs. The simulation results show that the proposed method has good performance in terms of the number of surviving sensors, network capacity and residual energy.

A new software-defined network architecture to solve energy balance problems in wireless sensor networks

Wireless sensor networks (WSNs) are an important part of the internet of things. In WSNs, the sensor battery is limited, and the entire network has a data aggregation node called Sink. When the sensor transmits data to the Sink in a multi-hop manner, the sensors around the Sink also help other sensors to forward the data to the Sink. This causes those sensors to die faster than other sensors. This problem is called energy hole problem. Previous solutions such as sensor uneven distribution schemes and clustering schemes are not only difficult to implement, but also increase the design cost of the system. In particular, traditional WSNs cannot be updated once deployed. In order to solve the above problem, we propose a method of combining SDN and WSNs. The simulation results show that the proposed method has good performance in terms of the number of surviving sensors, network capacity and residual energy.

Energy-Efficient and QoS-Aware Link Adaptation With Resource Allocation for Periodical Monitoring Traffic in SmartBANs

Limited energy resources of sensors and stringent quality-of-service (QoS) constraints in biomedical applications raise serious concerns when utilized in Wireless Body Area Networks (WBANs). The European Telecommunications Standards Institute (ETSI) Smart Body Area Network (SmartBAN) represents a standardized communication interface and protocol between a hub coordinator and a set of sensors, that has been designed with simplicity and low power in mind. This work presents an ETSI SmartBAN PHY and MAC configuration framework that remarkably lengthens sensors battery lifespan through reducing transceivers consumed energy. To that end, and taking into account the channel quality and packet error rate requisites of sensors, a mechanism to select between the different PHY transmission modes of the standard is proposed. This link adaptation scheme is combined with a resource allocation algorithm that derives the duration of the inter-beacon intervals and the transmission periods of sensors, while fulfilling traffic delay constraints and minimizing sensors transceivers energy consumption. Analytical expressions for packet error rate of all available PHY transmission modes, as well as for traffic delay, transceivers energy savings of hub and sensor nodes, and battery duration, are derived. Computer simulation results substantiate the efficacy of both, the presented QoS-aware adaptive transmission scheme and the resource allocation algorithm, in fulfilling the target packet error rate and delay requirements, while significantly expanding the battery duration, specially for sensors with long elapsed times between successive sensing intervals (up to a 515% increase and a 960% increase are obtained for the two considered simulation scenarios).

AUV-Aided Energy-Efficient Clustering in the Internet of Underwater Things

The integration of the Internet of Things (IoT) in our lives has become reality during the last decade. Mostly, IoT is considered a system of systems, where sensors make a big chunk of any IoT communication system. Similarly, in this paper, we introduce a notion of the Internet of Underwater Things (IoUT), where we consider a set of sensors deployed in the deep sea to form a communication network for any possible application, such as military, environmental, pollution surveillance, etc. Since it is too optimistic to recharge or replace sensor batteries in an underwater environment, efficient management of the available resources can extend the overall network lifetime. To do so, nodes clustering is a potential solution, however, exchange of multiple packets for cluster head selection requires a considerable amount of energy. In this paper, we present an autonomous unmanned vehicles (AUVs) assisted energy-efficient clustering (AEC) mechanism that introduces wake-up sleep cycle for the underwater nodes. AUV onus includes cluster creation, the cluster head nomination, and creation of a wakeup sleep schedule that relieves the additional burden from energy limited underwater sensor nodes. Due to no additional packets been exchanged, our protocol outperforms traditional clustering schemes and we validate this through simulations.

Considering Losses to Enhance Circuit Model Accuracy of Ultrasonic Wireless Power Transfer System

Ultrasonic wireless power transfer (UWPT) has been a promising technology to recharge the sensor batteries that locate in enclosed metal containers. An accurate and comprehensive analytical model is the key enabling tool for designing the UWPT system. In this article, a novel equivalent circuit model has been proposed for a typical UWPT system with two symmetric piezoelectric transducers bounding onto an aluminum plate coaxially. The proposed model is a two-port network equivalent circuit that can be described by T parameters. By considering the losses of the UWPT system, the accuracy of this model has been enhanced in comparison with the widely used equivalent circuit model, which ignores its losses. The experimental results show that the average error of output power decreases about 6.4 times in the proposed model compared to the existing model. Moreover, the proposed model is tested under various working conditions to validate the enhanced accuracy.

A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks

Wireless Sensor Networks (WSN) have lately been gaining momentum thanks to the hardware improvements and standardization software efforts. Moreover, the appearance of Internet of Things (IoT) and its reliance on sensors are helping to widely extend the usage of WSNs. However, such networks present drawbacks, mainly because of limited sensor batteries and their vulnerability against physical attacks due to the lack of protection and security. Additionally, not all the sensors inside the network have the same responsibility in terms of traffic handling. In this paper, we firstly analyze the fact that some nodes are more critical than others, considering the most critical node the one that, once incapacitated, causes the most deterioration on the network performance. Such performance is analyzed using two metrics, namely network latency and lifetime. We present a result comparison between a Mixed Integer Programming (MIP) model and a Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic for small networks. For bigger networks, GRASP meta-heuristic results are presented to understand how the network degrades as the number of both critical and network nodes increase, by distributing them into two different areas: fixed and incremental to maintain node density.

Home-Based Monitor for Gait and Activity Analysis.

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical evaluators. These measures are slightly subjective and are performed during a visit to a clinic or hospital and constitute therefore a point assessment. Point assessments can be influenced by daily patient condition or factors such as fatigue, motivation, and intercurrent illness. To enable home-based monitoring of gait and activity, a wearable magneto-inertial sensor (WMIS) has been developed. This device is a movement monitor composed of two very light watch-like sensors and a docking station. Each sensor contains a tri-axial accelerometer, gyroscope, magnetometer, and a barometer that record linear acceleration, angular velocity, the magnetic field of the movement in all directions, and barometric altitude, respectively. The sensors can be worn on the wrist, ankle, or wheelchair to record the subject's movements during the day. The docking station enables data uploading and recharging of sensor batteries during the night. Data are analyzed using proprietary algorithms to compute parameters representative of the type and intensity of the performed movement. This WMIS can record a set of digital biomarkers, including cumulative variables, such as total number of meters walked, and descriptive gait variables, such as the percentage of the most rapid or longest stride that represents the top performance of patient over a predefined period of time.

Embedded Solution for Atrial Fibrillation Detection Using Smart Wireless Body Sensors

Wearable sensors and IoT technologies have enabled real-time monitoring of vital signals and created new perception of medical patient-care strategies. Atrial fibrillation (AF) as the most common cardiac arrhythmia that is associated with increased risks of strokes can be detected in ECG signals using wireless body sensors. In depth, these devices can be deployed to capture and transmit raw ECG signals to a remote server for AF detection and further ECG classification. However, the transmission of raw ECG samples over energy-consuming wireless links limits the sensor's battery lifetime and questions the application's practicality. The design of an on-sensor AF energy efficient detection method is still an open research issue challenged by the sensor processing capability and its lifetime. This paper proposes a new ECG processing scheme for AF detection intended to be implemented in WBSN. It presents a low-complexity time-domain features' extraction method that combines the RR interval and p-wave absence in the ECG segment for higher AF detection accuracy. The proposed method achieved 100% sensitivity and 96% specifity while saving 92% of energy compared to full ECG data transmission. Thus demonstrating the technical feasibility of energy-efficiency for on-sensor AF detection.

Interview

Marco Bau Doctor Marco Bau from the University of Brescia in Italy, talks to Electronics Letters about the paper ‘Magnet-less electromagnetic contactless interrogation technique for unwired conductive resonators’, page 642. Our research group in the Department of Information Engineering operates in the fields of sensors, microsystems, electronics and measuring instrumentation. One of our topics of research is the investigation into the possibility of interrogating sensors without contact by means of an external unit which can be placed in the proximity of the sensor, a few centimetres away. In addition, we focus our efforts on using completely passive sensors, that is devices which do not have on-board active electronics or a power supply. Typically, these devices are mechanical or electromechanical resonating structures vibrating at their own resonant frequency and this frequency can change due to specific physical/chemical quantities. Our work investigates techniques to excite and sense the vibrations of such structures without any physical contact and, at the same time, challenging issues due to unfavourable signal-to-noise ratio. Sensors which can be interrogated without contact with an external unit are attractive for applications in environments and locations that are difficult to access and do not allow for cabled solutions, such as measurements inside sealed packages, enclosures and rotating parts, or even the human body. In these contexts, battery-powered systems are one possible solution, but they demand active electronics and energy sources be located on both the sensing device and the interrogation unit. Here, the main drawback is the necessity for periodical recharge/replacement of the sensor battery. As an alternative, the sensor and associated circuitry can be battery-less and powered by energy transfer from the reader, or by harvesting energy from the environment. But again, active electronics are required. However, sometimes this is not compliant with the application, such as for operation in high-temperature environments, where active electronics are not feasible. In our work we have adopted mechanical resonator structures which are only required to be electrically conductive. The excitation and detection of the vibrations exploit an electromagnetic principle, based on the induction of eddy currents on the surface of the resonator through a dedicated proximate 3-coil arrangement. Using only AC magnetic fields to generate vibrations in the resonator gives rise to non-linear behaviour, that is the mechanical structure vibrates at twice the frequency of the exciting magnetic field. In the phase of detection, this may require sophisticated electronic circuits and techniques. Alternatively, it is possible to use both DC and AC magnetic fields, so that excitation and vibration of the structure result in the same frequency. DC magnetic fields are typically generated by magnets, which in some circumstances can be impractical. In our work, magnets are avoided by exploiting a purposely designed 3-coil arrangement to generate both DC and AC magnetic fields. Our approach is particularly advantageous as, in addition to avoiding poling magnets, it allows the detection of vibrations through the same coil arrangement used for excitation. Indeed, we generate an additional AC probing magnetic field, which does not affect the excitation of the resonator, but is modulated by its movement. We detect the vibration-modulated magnetic field adopting a differential measurement technique which allows for an improvement to the signal-to-noise ratio. We expect that the results we have reported will be the starting point to improving the technique and exploring the limits of where it can be pushed in terms of downscaling of the sensing devices, possibly comprising also MEMS devices. We are confident that the proposed technique can be tested in selected practical applications for which we have received interest from industrial partners. Our focus is to further investigate different possibilities for contactless interrogation sensors limited not only to eddy-current based principles. Indeed, we have proposed, and are investigating, techniques still based on electromagnetic coupling, which extend the possibility of contactless interrogation to other typologies of resonating devices, like quartz crystal resonators (QCR), piezoelectric resonators, MEMS electromechanical resonators or electrical resonators based on inductor-capacitance tank circuits. In all these cases, we aim to adopt measurement techniques which can achieve accurate resonant-frequency readouts independent of the reading distance. The advantages and opportunities afforded by contactless interrogation techniques have been investigated in depth in recent years. We think that research in this field will be pursued extensively in the future with the aim of obtaining devices which will be energy-independent, environment-friendly, unobtrusive, disposable and can be interrogated on-demand and as needed. Maybe interrogating devices will be integrated in portable devices (such as smartphones) and will make this technology available for both industrial applications and every-day life.

Caching in Energy Harvesting Aided Internet of Things: A Game-Theoretic Approach

The Internet of Things (IoT) sensing service enables users to monitor the ambient environment by fetching data from IoT sensors. The explosive growth of mobile users and IoT applications injects massive traffic to the IoT network and also speeds up the drainage of sensor batteries. Caching at the IoT gateway (GW), which stores the IoT data and directly send them to the users, can avoid activating sensors too frequently, hence reducing the traffic in the IoT network as well as the energy consumption of sensors. To overcome the limited energy capacity of sensors, energy transmitters (ETs) are deployed to charge them. Practically, the GW and ETs may be owned by different operators, and the GW operator needs to incentivize ETs to provision the charging service. In this paper, we formulate a Stackelberg game in the cache-enabled energy harvesting aided IoT framework to improve the user quality of service. Caching strategies, incentive strategies, and ET transmission power strategies are jointly optimized to find the Stackelberg equilibrium by our proposed alternative direction approach. Simulation results elicit the benefits of our framework and demonstrate the performances of our proposed algorithm.

Two level data aggregation protocol for prolonging lifetime of periodic sensor networks

One big contributor in the future of the Internet of Things is the Periodic Sensor Networks (PSNs) because it has been used by many applications in real life. The main challenge in this type of networks is to gather the huge amount of data periodically in an energy saving way and then transmit them to the base station in order to extend the lifetime of PSN. Since the limited nature of the sensors batteries power, therefore, an energy-efficient data aggregation method is needed to optimize both energy and lifetime in PSNs. This article proposes a Two Level Data Aggregation (TLDA) Protocol for Prolonging the Lifetime of Periodic Sensor Networks. TLDA works in a periodic way. Each period consists of two data aggregation levels. The first level of data aggregation is applied at the sensor node. This level includes data collection, the sliding window to generate a varying number of segments with different lengths, and data aggregation using Adaptive Piecewise Constant Approximation technique to reduce the amount of data collected by each sensor. The second level is applied at the aggregator. It includes grouping received data sets based on the chaining hash table with SAX quantization method, finding and lowering the duplicate sets, finding and merging the duplicate readings, and transmit the aggregated data to the sink. Extensive simulation results are conducted using OMNeT++ network simulator and based on real data of sensor network to show the efficiency of the TLDA protocol compared with two existing methods.

A Tunable Resonance Cantilever for Cardiac Energy Harvesting.

Energy harvesting from cardiac motion is an attractive means to avoid the use of batteries in implantable sensors and pacemakers. A single implantable device would ideally integrate both sensing and self-powering functionality. This work describes a novel electromagnetic system that achieves high sensitivity detection of the heart rate while simultaneously providing adaptive energy harvesting capability using a tunable resonance cantilever mechanism. Our prototype design exhibits tunability of resonant frequency across the range of physiologic heart rates at a combination of lengths and angular orientations. Our initial prototype also produces between 3.0[Formula: see text]W and 20.6[Formula: see text]W of power at heart rates of 79-243 bpm, respectively. The prototype device can harvest sufficient energy to sustain implantable cardiac devices such as a leadless pacemaker. The system in this paper has the potential to eliminate batteries in certain implantable cardiac devices and thereby improve overall patient monitoring and treatment.

A Novel and Enhanced Distributed Clustering Methodology for Large Scale Wireless Sensor Network Fields

Wireless sensor network (WSN) is a grid of sensors possessing processor unit and trivial memory unit implanted on them. Trustworthy packet forwarding from nodes to sink seems to be the most substantial purpose of this sensor network. The customary routing algorithms could not be employed at this juncture since the sensor battery power is limited. To provide energy proficiency, sensors are normally grouped as non-overlapping groups. This research work provides a transitory summary on clustering procedures in sensor networks. An energy-efficient distributed clustering approach for impenetrable sensor networks, the Weight based clustering Low Energy Adaptive Clustering Hierarchy (WC-LEACH) is proposed and the outcomes are assessed in contradiction with the prevailing Low Energy Adaptive Clustering Hierarchy (LEACH) and Hybrid Energy Efficient Distributed Clustering (HEED) methodologies. Simulation results obviously display an exceptional enhancement in packet delivery ratio, reduced packet loss, reduced energy consumption, increased throughput and increased lifetime for WSNs.

Resource allocation for relay-aided underwater acoustic sensor networks with energy harvesting

Underwater acoustic sensor networks play a significant role in ocean observation, pollution monitoring and underwater remote control. Different from conventional sensor networks, transmission efficiency and lifetime for nodes in underwater acoustic sensor networks is vital since it is difficult to change batteries of buoy relays and underwater sensors. In this paper, two resource allocation algorithms for energy harvesting time and transmit power allocation in underwater acoustic sensor networks with single and multiple wireless energy harvesting buoy nodes are proposed to maximize the sum rate and energy efficiency. Both of the circuit power consumption and transmission power consumption are considered as the power consumption of the system. We first propose an optimal algorithm to allocate the time of energy harvesting slot for sum rate maximization. From the derivation, we show that the sum rate of the network grows as the transmission power increases. However, when the transmission power is extremely large, the energy efficiency of the network starts to decrease. Thus a joint algorithm with energy harvesting time and transmit power allocation is further provided for energy efficiency maximization. Simulation results validate the sum rate improvement under our proposed sum rate maximization algorithm and energy efficiency improvement under the proposed energy efficiency maximization algorithm.

Battery Charger Prototype Design for Tire Pressure Sensor Battery Recharging.

One of the important devices in a vehicle is the tire pressure sensor. This device couples with other instruments inside the vehicle assisting the drivers in knowing the correct information about their vehicle’s tire pressure. This information helps improve vehicle handling, increases gas mileage, and extends tire lifespan. Once mounted inside the tire, the tire pressure sensor is a stand-alone device. It is powered by a battery that has a limited operating life. Due to it being mounted inside the tire, the driver does not frequently check tire pressure sensor battery. If the battery runs out, battery replacement is not an effective option. This work presents a battery charging prototype that recharges the tire pressure sensor battery. The developed device uses electromagnetic principles to wirelessly transmit power to a device that needs power. We designed a prototype and conducted some laboratory scale experiments. Experimental and validation were based on a tire pressure sensor developed by Kenda Rubber Ind. Co., Ltd, Taiwan (R.O.C). This tire pressure sensor consumes power from a 4.8 V 700 mAh Li-ion rechargeable battery. The experimental results show that the prototype can transmit 4.9 V induction voltage. The maximum current is up to 850 mA with the optimum transmission distance at around of 1.5 cm. This prototype recharges the tire pressure sensor battery wirelessly to extend its battery-power life.

Optimal Energy Allocation in Multisensor Estimation Over Wireless Channels Using Energy Harvesting and Sharing

We investigate the optimal power control for multisensor estimation of correlated random Gaussian sources. A group of wireless sensors obtains local measurements and transmits them to a remote fusion center (FC), which reconstructs the measurements using the minimum mean-square error estimator. All the sensors are equipped with an energy harvesting module and a transceiver unit for wireless, directed energy sharing between neighboring sensors. The sensor batteries are of finite storage capacity and prone to energy leakage. Our aim is to find optimal power control strategies, which determine the energies used to transmit data to the FC and shared between sensors, so as to minimize the long-term average distortion over an infinite horizon. We assume centralized causal information of the harvested energies and channel gains, which are generated by independent finite-state stationary Markov chains. The optimal power control policy is derived using a stochastic predictive control formulation. We also investigate the structure of the optimal solution, a Q-learning based suboptimal power control scheme and two computationally simple and easy-to-implement heuristic policies. Extensive numerical simulations illustrate the performance of the considered policies.