Outdoor Sensor Research Articles

Novel energy savings method considering extra sensor battery discharge time for fish farming applications

Energy savings in Wireless Sensor Networks for fish farms is necessary and beneficial. We present a novel energy savings method that minimizes the interpolation errors of sensors' measurements. Sensors follow a working cycle in which they are active when measuring data, and inactive or suspended when data is interpolated from the above measurements and consumed energy is reduced. To our knowledge, we are the first to implement interpolation for energy savings. We improved that model to describe the non-linear property of the consumed energy in the batteries, adding a new variable that explains their real behavior. Several experiments with a prototype of a Wireless Sensor Network with pH, water temperature, and ambient temperature sensors are implemented to validate our method. We made a series of measurements to determine the actual energy savings at each sensor and compared these savings with those predicted by each theory model. The results show that the model is more accurate, presenting less than 5 % prediction errors which does not affect fish growth. Furthermore, our paper introduces an energy-saving method for extending WSN lifetime by modeling the non-linear power consumption of sensors' batteries. We propose a new mathematical optimization formulation using an efficient interpolation mechanism that operates in real-time. A real-scale WSN prototype installed over water validates and refines our method. Finally, we showed that the number of interpolated values is of a broader range for aquatic sensors than for outdoor sensors such as ambient temperature. That is, energy savings for fish farming is acceptable.

Assessing bias in personal exposure estimates when indoor air quality is ignored: A comparison between GPS-enabled mobile air sensor data and stationary outdoor sensor data

Neglecting indoor air quality in exposure assessments may lead to biased exposure estimates and erroneous conclusions about the health impacts of exposure and environmental health disparities. This study assessed these biases by comparing two types of personal exposure estimates for 100 individuals: one derived from real-time particulate matter (PM2.5) measurements collected both indoors and outdoors using a low-cost portable air monitor (GeoAir2.0) and the other from PurpleAir sensor network data collected exclusively outdoors. The PurpleAir measurement data were used to create smooth air pollution surfaces using geostatistical methods. To obtain mobility-based exposure estimates, both sets of air pollution data were combined with the individuals' GPS tracking data. Paired-sample t-tests were then performed to examine the differences between these two estimates. This study also investigated whether GeoAir2.0- and PurpleAir-based estimates yielded consistent conclusions about gender and economic disparities in exposure by performing Welch's t-tests and ANOVAs and comparing their t-values and F-values. The study revealed significant discrepancies between GeoAir2.0- and PurpleAir-based estimates, with PurpleAir data consistently overestimating exposure (t = 5.94; p < 0.001). It also found that females displayed a higher average exposure than males (15.65 versus. 8.55 μg/m3) according to GeoAir2.0 data (t = 4.654; p = 0.055), potentially due to greater time spent indoors engaging in pollution-generating activities traditionally associated with females, such as cooking. This contrasted with the PurpleAir data, which indicated higher exposure for males (43.78 versus. 46.26 μg/m3) (t = 3.793; p = 0.821). Additionally, GeoAir2.0 data revealed significant economic disparities (F = 7.512; p < 0.002), with lower-income groups experiencing higher exposure—a disparity not captured by PurpleAir data (F = 0.756; p < 0.474). These findings highlight the importance of considering both indoor and outdoor air quality to reduce bias in exposure estimates and more accurately represent environmental disparities.

Diurnal variation of indoor air pollutants and their influencing factors in educational buildings: A case study using LASSO-based ANNs

This study explores the diurnal variations and influencing factors of PM2.5, NO2, and ozone concentrations in educational buildings. Utilizing an integrated system of indoor and outdoor sensors, building automation control networks, and walk-through inspections, air quality data along with relevant characteristics were collected from ten educational buildings in Central Florida. Advanced Neural Network models (RNNs and CNNs), including the Long Short-Term Memory (LSTM) and the Attention Temporal Convolutional Network (ATCN) algorithms based on the Least Absolute Shrinkage and Selection Operator (LASSO), were developed to accurately identify diurnal patterns in indoor air quality (IAQ) and the differences in influencing factors. The findings indicate greater variability in diurnal differences and factors influencing indoor NO2 and ozone concentrations compared to PM2.5. Although the factors influencing day and night PM2.5 levels were similar, there were significant differences in the contribution weights of these factors. Optimized RNNs and CNNs significantly outperformed standard Artificial Neural Network (ANN) models in dynamically simulating and predicting target pollutants. Comparative analysis of the root-mean-square error (RMSE) demonstrated that LASSO-LSTM models comprehensively outperformed LASSO-ATCN models by averaging 13.4% (p < 0.05). These results can be referenced in studies concerning Indoor Air Quality (IAQ) control conducted in similar environmental settings.

Low-Cost Indoor Sensor Deployment for Predicting PM2.5 Exposure.

Indoor air quality is critical to human health, as individuals spend an average of 90% of their time indoors. However, indoor particulate matter (PM) sensor networks are not deployed as often as outdoor sensor networks. In this study, indoor PM2.5 exposure is investigated via 2 low-cost sensor networks in Pittsburgh. The concentrations reported by the networks were fed into a Monte Carlo simulation to predict daily PM2.5 exposure for 4 demographics (indoor workers, outdoor workers, schoolchildren, and retirees). Additionally, this study compares the effects of 4 different correction factors on reported concentrations from the PurpleAir sensors, including both empirical and physics-based corrections. The results of the Monte Carlo simulation show that mean PM2.5 exposure varied by 1.5 μg/m3 or less when indoor and outdoor concentrations were similar. When indoor PM concentrations were lower than outdoor, increasing the time spent outdoors on the simulation increased exposure by up to 3 μg/m3. These differences in exposure highlight the importance of carefully selecting sites for sensor deployment and show the value of having a robust low-cost sensor network with both indoor and outdoor sensor placement.

Machine learning classifiers for fall detection leveraging LoRa communication network

Today, health monitoring relies heavily on technological advancements. This study proposes a low-power wide-area network (LPWAN) based, multinodal health monitoring system to monitor vital physiological data. The suggested system consists of two nodes, an indoor node, and an outdoor node, and the nodes communicate via long range (LoRa) transceivers. Outdoor nodes use an MPU6050 module, heart rate, oxygen pulse, temperature, and skin resistance sensors and transmit sensed values to the indoor node. We transferred the data received by the master node to the cloud using the Adafruit cloud service. The system can operate with a coverage of 4.5 km, where the optimal distance between outdoor sensor nodes and the indoor master node is 4 km. To further predict fall detection, various machine learning classification techniques have been applied. Upon comparing various classifier techniques, the decision tree method achieved an accuracy of 0.99864 with a training and testing ratio of 70:30. By developing accurate prediction models, we can identify high-risk individuals and implement preventative measures to reduce the likelihood of a fall occurring. Remote monitoring of the health and physical status of elderly people has proven to be the most beneficial application of this technology.

A Literature Review for Understanding the Development of Smart Parking Systems

Smart parking systems that use AI, data analytics, and IoT are a result of urbanization and rising automobile utilization. These systems are designed to enhance user experience, shorten search times, and make the most of available space. AI analyzes real-time data, proposes open places, and projects demand in the future. Infrastructure costs, stakeholder cooperation, and system compatibility continue to be issues, nevertheless. To maintain user confidence, privacy, and ethical usage in the study of smart parking systems, in-depth literature reviews are essential. The systematic literature review (SLR) method was used to examine AI-based smart parking solutions, such as wireless sensor networks, ultrasonic sensor nodes, reservation-based systems, intelligent parking guidance, IoT-based on-street infraction monitoring, central parking management systems, and energy-efficient automated solutions. Budgetary restrictions, stakeholder participation, interoperability concerns, data privacy, security, and moral ambiguities are all problems. To test scenarios, understand rules, processes, and algorithms in limited contexts, researchers need to develop reliable outdoor sensor and data technologies for outside application.

Technical note: Identifying a performance change in the Plantower PMS 5003 particulate matter sensor

Communities, researchers, and citizen scientists have increasingly been deploying low-cost particulate matter (PM) sensors to understand pollution levels and personal pollution exposure. Currently, tens of thousands of these low-cost sensors have been deployed throughout the world. One of the most commonly used sensors is the Plantower PMS 5003. In 2021, PurpleAir (one of the largest sensor networks) noted a change in the performance of their PM sensors that could be identified by the ratio of PM counts in two size bins (≥0.3 μm and ≥0.5 μm). This study confirmed that in June 2021, an updated version of the Plantower PMS 5003 (PMS-U) was deployed between approximately June 2021 and January 2022. Our laboratory study revealed that the PMS-Us PM2.5 concentrations were biased low for raw concentrations (CF = 1) below 16 μg/m3. Our field study also suggests that this bias translates into an approximately 3 μg/m3 lower PM2.5 concentrations (raw) over the long term, and between March to May 2023, more than 10% of outdoor sensors in the PurpleAir network are PMS-Us. This level of bias would not affect some uses of low-cost sensor networks, such as during wildfires. However, this bias could be important for health or environmental justice studies. This study also developed and evaluated a correction method for PMS-U sensors to reduce the observed bias in PMS-U sensors.

Analysis of Simultaneous WPT in Ultra-Low-Power Systems with Multiple Resonating Planar Coils

This paper analyses the conceptual application of a wireless power transfer (WPT) system with multiple resonators supplying outdoor sensors using a mobile charger. The solution is based on the idea of using sensors, located in open space, to monitor environmental parameters. Instead of the typical two-coil WPT with a single charger, energy transfer is realized simultaneously, using a group of identical planar coils as transmitters and receivers connected to the independent power supply circuits of each sensor and microcontroller. By isolating these charged circuits, a higher reliability and powering flexibility of the weather station can be achieved. The concept of the proposed system was discussed, and it was proposed to include the main devices in it. A theoretical analysis was performed considering all mutual couplings and the skin effect; hence, the system is characterized by a matrix equation and sufficient formulae are given. The calculations were verified experimentally for different frequencies, two possible distances between the transmitters and receivers, and equivalent loads. Both the efficiency and load power are compared and discussed, showing that this solution can provide power to ultra-low-power devices, yet the efficiency must still be improved. At the small distance between the transmitting and receiving coils (5 mm), the maximum efficiency value was about 40%, with a load resistance of 10 Ω. By doubling the distance between the coils, the efficiency of the WPT system decreased by three times.

Measuring the fine particulate exposure levels of building occupants using localized sensors

Rising levels of Environmental Air Pollution (EAP) caused by wildfires and traffic emissions impact Indoor Air Quality (IAQ) by penetrating buildings through air conditioning intakes and door and window openings. Exposure to fine Particulate Matter (PM2.5) causes building occupant discomfort and significant health issues. Hence, it is vital to continuously monitor the PM2.5 exposure level and the general IAQ of buildings. This study uses Internet of Things (IoT) sensors to investigate the temporal and spatial correlations between indoor and outdoor PM2.5 concentrations in a university building in Sydney, Australia, over five months. Sensor measurements are used to determine the Indoor to Outdoor (I/O) ratio and Exceedance Index (E-index). The study timeline included impacts associated with Hazard Reduction Burning (HRB) and localized peak traffic flow. The findings reveal that the closest indoor area to the building entrance exceeded double the World Health Organization (WHO) PM2.5 recommended threshold for more than 80% of the study period. Results also confirm a negative correlation between the distance from ground level and indoor PM2.5 exposure. An hourly analysis shows that the PM2.5 concentrations in Winter increase overnight. During HRB in Winter, the I/O ratios increased by up to 200% on average during regular HVAC operating hours. Localized outdoor readings were also compared with the nearest regional air quality monitoring station (RAQMS). Those results indicate that the average PM2.5 for the local outdoor sensor was approximately 2.5 times higher than the nearest RAQMS, confirming that regional stations may not be reliable references for localized PM2.5 concentrations.

MPPT Study of Outdoor Sensing Terminal PV System Based on Improved PSO Algorithm

With the popularity of wireless sensor use, the problem of supplying energy to its outdoor nodes is becoming more and more prominent. An outdoor wireless sensing terminal photovoltaic system is proposed to address the problem of outdoor wireless sensing terminal node power supply. The maximum power tracking (MPPT) is achieved by Adaptive weight adjustment Particle swarm optimization (AwaPSO) to achieve fast tracking and stable and reliable output, so that the Buck main circuit output of the PV system can meet the working requirements of the outdoor sensor terminal. The simulation model is built by Matlab/Simulink, and the simulation results show that the improved PSO algorithm can track the maximum power quickly and output stably and reliably, with the stabilization time shortened to 0.041s and the tracking error up to 0.625%, and its speed and stability can meet the outdoor wireless sensing terminal PV power supply requirements.

Factors influencing indoor air pollution in buildings using PCA-LMBP neural network: A case study of a university campus

This study investigated indoor air quality (IAQ) and its association with building characteristics and environmental factors in eleven different campus buildings in Gainesville, Florida. Integrated indoor and outdoor sensor systems are built and installed to measure the levels of airborne particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), relative humidity (RH), and temperature continuously in 10 min intervals for two weeks for each case building. Twenty building-related characteristics were collected through a walkthrough-based survey, HVAC historical data, and construction drawings. Through these data, a PCA-assisted Levenberg-Marquardt Backpropagation (LMBP) neural network model was developed for rapidly and accurately analyzing and predicting the interaction between IAQ and its affecting factors. Factors with significant contributions to indoor exposure were may mostly be determined by outdoor sources. This is evidenced by the strong associations that were found between indoor PM(2.5-10) and O3 values and their corresponding outdoor values and factors, including distance from the major traffic (DFMT), cracks occurring, outdoor temperature, and humidity. Indoor NO2 concentrations were affected by DFMT, indoor O3, indoor RH, number of air grilles, room volume, and window-to-wall ratio. Also, the comparison shows that the PCA-LMBP model outperforms the traditional BP-ANN and multi-linear regression methods. The average values of 1.34, 2.53, and 4.86 were obtained for the root-mean-square error (RMSE) of PCA-LMBP, BP-ANN, and MLR models, respectively. These results can be accordingly referred for the follow-up studies that analyze IAQ in similar building and environmental conditions.

A Multimodal Sensing Platform for Interdisciplinary Research in Agrarian Environments.

Agricultural and environmental monitoring programs often require labor-intensive inputs and substantial costs to manually gather data from remote field locations. Recent advances in the Internet of Things enable the construction of wireless sensor systems to automate these remote monitoring efforts. This paper presents the design of a modular system to serve as a research platform for outdoor sensor development and deployment. The advantages of this system include low power consumption (enabling solar charging), the use of commercially available electronic parts for lower-cost and scaled up deployments, and the flexibility to include internal electronics and external sensors, allowing novel applications. In addition to tracking environmental parameters, the modularity of this system brings the capability to measure other non-traditional elements. This capability is demonstrated with two different agri- and aquacultural field applications: tracking moth phenology and monitoring bivalve gaping. Collection of these signals in conjunction with environmental parameters could provide a holistic and context-aware data analysis. Preliminary experiments generated promising results, demonstrating the reliability of the system. Idle power consumption of 27.2 mW and 16.6 mW for the moth- and bivalve-tracking systems, respectively, coupled with 2.5 W solar cells allows for indefinite deployment in remote locations.

A Machine-Learning-Based Analysis of the Relationships between Loneliness Metrics and Mobility Patterns for Elderly.

Loneliness and social isolation are subjective measures associated with the feeling of discomfort and distress. Various factors associated with the feeling of loneliness or social isolation are: the built environment, long-term illnesses, the presence of disabilities or health problems, etc. One of the most important aspect which could impact feelings of loneliness is mobility. In this paper, we present a machine-learning based approach to classify the user loneliness levels using their indoor and outdoor mobility patterns. User mobility data has been collected based on indoor and outdoor sensors carried on by volunteers frequenting an elderly nursing house in Tampere region, Finland. The data was collected using Pozyx sensor for indoor data and Pico minifinder sensor for outdoor data. Mobility patterns such as the distance traveled indoors and outdoors, indoor and outdoor estimated speed, and frequently visited clusters were the most relevant features for classifying the user’s perceived loneliness levels.Three types of data used for classification task were indoor data, outdoor data and combined indoor-outdoor data. Indoor data consisted of indoor mobility data and statistical features from accelerometer data, outdoor data consisted of outdoor mobility data and other parameters such as speed recorded from sensors and course of a person whereas combined indoor-outdoor data had common mobility features from both indoor and outdoor data. We found that the machine-learning model based on XGBoost algorithm achieved the highest performance with accuracy between and for indoor, outdoor, and combined indoor-outdoor data. We also found that Lubben-scale based labelling of perceived loneliness works better for both indoor and outdoor data, whereas UCLA scale-based labelling works better with combined indoor-outdoor data.

Network of low-cost air quality sensors for monitoring indoor, outdoor, and personal PM2.5 exposure in Seattle during the 2020 wildfire season

The increased frequency of wildfires in the Western United States has raised public awareness of the impact of wildfire smoke on air quality and human health. Exposure to wildfire smoke has been linked to an increased risk of cancer and cardiorespiratory morbidity. Evidence-driven interventions can alleviate the adverse health impact of wildfire smoke. During wildfires, public health guidance is based on regional air quality data with limited spatiotemporal resolution. Recently, low-cost air quality sensors have been used in air quality studies, given their ability to capture high-resolution spatiotemporal data. We demonstrate the use of a network of low-cost particulate matter (PM) sensors to gather indoor and outdoor PM2.5 data from seven locations in the urban Seattle area, along with a personal exposure monitor worn by a resident living in one of these locations during the 2020 Washington wildfire event. The data were used to determine PM concentration indoor/outdoor (I/O) ratios, PM reduction, and personal exposure levels. The result shows that locations equipped with high-efficiency particulate air (HEPA) filters and HVAC filtration systems had significantly lower I/O ratios (median I/O = 0.43) than those without air filtration (median I/O = 0.82). The median PM2.5 reduction for the locations with HEPA is 58% compared to 20% for the locations without HEPA. The outdoor PM sensor showed a high correlation to the nearby regional air quality monitoring stations (pre-calibration R2 = 0.92). The personal monitor showed higher variance in PM measurements as the user moved through different microenvironments and could not be fully characterized by the network of indoor or outdoor monitors. The findings imply that evidence-based interventions can be developed to reduce pollution exposure when combining data from indoor and outdoor sensors. Personal exposure monitoring captured temporal spikes in PM exposure.

Spatial–temporal variability and health impact of particulate matter during a 2019–2020 biomass burning event in Southeast Asia

To understand the characteristics of particulate matter (PM) in the Southeast Asia region, the spatial–temporal concentrations of PM10, PM2.5 and PM1 in Malaysia (Putrajaya, Bukit Fraser and Kota Samarahan) and Thailand (Chiang Mai) were determined using the AS-LUNG V.2 Outdoor sensor. The period of measurement was over a year from 2019 to 2020. The highest concentrations of all sizes of PM in Putrajaya, Bukit Fraser and Kota Samarahan were observed in September 2019 while the highest PM10, PM2.5 and PM1 concentrations in Chiang Mai were observed between March and early April 2020 with 24 h average concentrations during haze days in ranges 83.7–216 µg m−3, 78.3–209 µg m−3 and 57.2–140 µg m−3, respectively. The average PM2.5/PM10 ratio during haze days was 0.93 ± 0.05, which was higher than the average for normal days (0.89 ± 0.13) for all sites, indicating higher PM2.5 concentrations during haze days compared to normal days. An analysis of particle deposition in the human respiratory tract showed a higher total deposition fraction value during haze days than on non-haze days. The result from this study indicated that Malaysia and Thailand are highly affected by biomass burning activity during the dry seasons and the Southwest monsoon.

Study of RSSI Accuracy for Outdoor Sensor Localization

The movement characteristics that nodes exhibit will bring serious problems in a Wireless Sensor Network (WSN) because most of the protocols do not adapt to node movement. This requires a mobility-aware evaluation technique to locate nodes, forecast communication quality, trace the movement trajectory and formulate thresholds to initiate future handover. One off-the-shelf technology using the Received Signal Strength Indicator (RSSI) of wireless radio is extensively adopted because of the small expense. A node can compute the relative distance between it and its partner by using RSSI readings. However, the measurement results of RSSI fluctuate significantly due to the effects of shadowing and fading. This paper is oriented to investigate the localization accuracy of RSSI in an outdoor ambient condition. To do this, some dynamic and stationary tests are carried out, a calibration line displaying the one-to-one match between RSSI and distance is mapped, five filtering approaches are developed to alleviate the fluctuation of movement curves, and the alleviation effect is measured by calculating the Root Mean Square Error (RMSE) values and by converting the time-domain filtering results into the Fast Fourier Transform (FFT) spectrum. Although the optimized RMSE is reduced to 0.86 and the noise FFT amplitude is less than 1[Formula: see text]dBm, it may still have a single RSSI value corresponding to more than one distance. Moreover, these distances can differ as much as 2.8[Formula: see text]m. Since the evaluation error is not acceptable for most cases, it is inaccurate for a mobile node to measure the distance from its partner rely on RSSI readings under outdoor scenarios.

Modeling Communication over Terrain for Realistic Simulation of Outdoor Sensor Network Deployments

Popular wireless network simulators have few available propagation models for outdoor Internet of Things applications. Of the available models, only a handful use real terrain data, yet an inaccurate propagation model can skew the results of simulations. In this article, we present TerrainLOS, a low-overhead propagation model for outdoor Internet of Things applications that uses real terrain data to determine whether two nodes can communicate. To the best of our knowledge, TerrainLOS is the first terrain-aware propagation model that specifically targets outdoor IoT deployments and that uses height maps to represent terrain. In addition, we present a new terrain classification method based on terrain “roughness,” which allows us to select a variety of terrain samples to demonstrate how TerrainLOS can capture the effects of terrain on communication. We also propose a technique to generate synthetic terrain samples based on “roughness.” Furthermore, we implemented TerrainLOS in the COOJA-Contiki network simulation/emulation platform, which targets IoT deployments and uses TerrainLOS to evaluate how often a network is fully connected based on the roughness of terrain, as well as how two popular power-aware routing protocols, RPL and ORPL, perform when terrain is considered.

Long-term evaluation of a low-cost air sensor network for monitoring indoor and outdoor air quality at the community scale

Given the growing interest in community air quality monitoring using low-cost sensors, 30 PurpleAir II sensors (12 outdoor and 18 indoor) were deployed in partnership with community members living adjacent to a major interstate freeway from December 2017- June 2019. Established quality assurance/quality control techniques for data processing were used and sensor data quality was evaluated by calculating data completeness and summarizing PM2.5 measurements. To evaluate outdoor sensor performance, correlation coefficients (r) and coefficients of divergence (CoD) were used to assess temporal and spatial variability of PM2.5 between sensors. PM2.5 concentrations were also compared to traffic levels to assess the sensors’ ability to detect traffic pollution. To evaluate indoor sensors, indoor/outdoor (I/O) ratios during resident-reported activities were calculated and compared, and a linear mixed-effects regression model was developed to quantify the impacts of ambient air quality, microclimatic factors, and indoor human activities on indoor PM2.5. In general, indoor sensors performed more reliably than outdoor sensors (completeness: 73% versus 54%). All outdoor sensors were highly temporally correlated (r > 0.98) and spatially homogeneous (CoD<0.06). The observed I/O ratios were consistent with existing literature, and the mixed-effects model explains >85% of the variation in indoor PM2.5 levels, indicating that indoor sensors detected PM2.5 from various sources. Overall, this study finds that community-maintained sensors can effectively monitor PM2.5, with main data quality concerns resulting from outdoor sensor data incompleteness.

A novel air quality monitoring and improvement system based on wireless sensor and actuator networks using LoRa communication.

In this paper, we study the air quality monitoring and improvement system based on wireless sensor and actuator network using LoRa communication. The proposed system is divided into two parts, indoor cluster and outdoor cluster, managed by a Dragino LoRa gateway. Each indoor sensor node can receive information about the temperature, humidity, air quality, dust concentration in the air and transmit them to the gateway. The outdoor sensor nodes have the same functionality, add the ability to use solar power, and are waterproof. The full-duplex relay LoRa modules which are embedded FreeRTOS are arranged to forward information from the nodes they manage to the gateway via uplink LoRa. The gateway collects and processes all of the system information and makes decisions to control the actuator to improve the air quality through the downlink LoRa. We build data management and analysis online software based on The Things Network and TagoIO platform. The system can operate with a coverage of 8.5 km, where optimal distances are established between sensor nodes and relay nodes and between relay nodes and gateways at 4.5 km and 4 km, respectively. Experimental results observed that the packet loss rate in real-time is less than 0.1% prove the effectiveness of the proposed system.

An autonomous bioluminescent bacterial biosensor module for outdoor sensor networks, and its application for the detection of buried explosives

We describe a miniaturized field-deployable biosensor module, designed to function as an element in a sensor network for standoff monitoring and mapping of environmental hazards. The module harbors live bacterial sensor cells, genetically engineered to emit a bioluminescent signal in the presence of preselected target materials, which act as its core sensing elements. The module, which detects and processes the biological signal, composes a digital record that describes its findings, and can be transmitted to a remote receiver. The module is an autonomous self-contained unit that can function either as a standalone sensor, or as a node in a sensor network. The biosensor module can potentially be used for detecting any target material to which the sensor cells were engineered to respond. The module described herein was constructed to detect the presence of buried landmines underneath its footprint. The demonstrated detection sensitivity was 0.25 mg 2,4-dinitrotoluene per Kg soil.