Energy harvesting (EH) is the process of capturing and storing energy from external sources or the ambient environment. The EH devices have found various emerging applications, particularly, in healthcare sector. Kinetic-based micro energy-harvesting is a promising technology that could prolong the lifetime of batteries in small wearable or implantable devices. In this paper, using a mathematical model of a Coulomb-force parametric generator, we analyze the dependency of the output power on the electrostatic force in this micro-harvester. We propose a very low complexity strategy to adaptively change the electrostatic force in order to maximize the harvested power. Simulation results using the human acceleration measurements confirm the effectiveness of the proposed strategy.

Miniaturized wearable or implantable medical sensors (or actuators) are often used in the Internet of Things (IoT) technologies in healthcare applications. However, their limited source of power is becoming a bottleneck for the pervasive use of these devices, especially, as their functionality increases. Kinetic-based micro-energy harvesters can generate power through the natural human body motion. Therefore, they can be an attractive solution to supplement the source of power in medical wearables or implants. The architecture based on the Coulomb force parametric generator (CFPG) is the most viable micro-harvester solution for generating power from human motion. This article proposes three methods: a linear estimation approach, a multi-armed bandit algorithm, and a min–max-based approach to adaptively estimate the desirable electrostatic force in a CFPG using the input acceleration waveform. Through extensive simulations, the performance of the proposed methods in maximizing the output power of the micro-harvester is evaluated.

A commonly used methodology to estimate the proximity of two individuals in an automatic exposure notification system uses the signal strength of the Bluetooth signal from their mobile phones. However, there is an underlying error in Bluetooth-based proximity detection that can result in wrong exposure decisions. A wrong decision in the exposure determination leads to two types of errors: false negatives and false positives. A false negative occurs when an exposed individual is incorrectly identified as not exposed. Similarly, a false positive occurs when a non-exposed individual is mistakenly identified as exposed. Both errors have costly implications and can ultimately determine the effectiveness of Bluetooth-based automatic exposure notification in containment of pandemics such as COVID-19. In this paper, we present a platform that allows for the analysis of the system performance under various parameters. This platform enables us to gain a better understanding on how the underlying technology error propagates through the contact tracing system. Preliminary results show the considerable impact of the Bluetooth-based proximity estimation error on false exposure determination. Alternatively, using this platform, analysis can be performed to determine the acceptable accuracy level of a proximity detection mechanism in order to have a more effective contact tracing solution.

Current automatic exposure notification apps primarily operate based on hard distance/time threshold guidelines (e.g., 2 m/15 min in the United States) to determine exposures due to close contacts. However, the possibility of virus transmission through inhalation for distances over the specified distance threshold might necessitate consideration of soft distance/time thresholds to accommodate all transmission scenarios. In this paper, using a simplifying approximation on the instantaneous rate of the viral exposure versus distance, we extend the definition of "contact" by proposing a soft distance/time threshold which includes the possibility of getting exposed at any distance (within certain limits) around an infected person. We then analyze the performance of automatic exposure notification with Bluetooth-based proximity detection by comparing the exposure results when soft or hard thresholds are used. This study is done through an agent-based simulation platform that allows for a comprehensive analysis using several system parameters. By tuning the parameters of the proposed soft thresholds, a more accurate determination of possible exposures at any distance would be possible. This would enhance the effectiveness of an automatic contact tracing system. Our results indicate the noticeable impact of using the soft distance/time threshold on the exposure detection accuracy.

The proximity detection mechanism in current automatic exposure notification systems is typically based on the Bluetooth signal strength from the individual’s mobile phone. However, there is an underlying error in this proximity detection methodology that could result in wrong exposure decisions i.e., false negatives and false positives. A false negative error happens if a truly exposed individual is mistakenly identified as not exposed. This misidentification could result in further spread of the virus by the exposed (yet undetected) individual. Likewise, when a non-exposed individual is incorrectly identified as exposed, a false positive error occurs. This could lead to unnecessary quarantine of the individual; and therefore, incurring further economic cost. In this paper, using a simulation platform and a notion of proximity detection error, we investigate the performance of the system in terms of false exposure determinations. Knowledge of how the Bluetooth-based proximity detection error impacts such false determinations and identification of methodologies that can reduce this impact will be helpful to enhance the effectiveness of an automatic contact tracing system. Our preliminary results indicate the substantial impact of the proximity estimation error on the exposure detection accuracy. The results also suggest how proper filtering of distance measurements may reduce this impact.

In this paper we investigate the feasibility of a simple wearable system that can be used at home to detect or monitor excess fluid buildup in the lungs. This is a medical condition referred to as pulmonary edema. A methodology has been developed to computationally emulate human lungs with various levels of fluid in the alveoli. The proposed wearable system is composed of several small wearable antennas located on the chest and back area. The antennas will operate at MedRadio frequency band and will be optimized for signal penetration through the body. The frequency and time responses of the communication channel between these antennas for the lung models with varying levels of fluid have been measured and analyzed. The results show a correlation between the channel response and the level of fluids inside the lungs. This correlation can potentially be exploited by a simple wearable system to predict the onset of pulmonary edema for patients living in remote areas or people who need to be continuously monitored.

Comprehensive study of wireless communication for ingestible electronics is a very challenging task. To initiate the study on wireless capsule endoscopy, an innovative immersive platform containing an enhanced 3D human body model has been developed. This platform allows for flexible placement of a capsule inside the gastrointestinal (GI) tract and multiple receivers around the abdomen area. It is shown that any directionality (or equivalently null) in the antenna radiation pattern can be exploited to sense changes in the orientation of the capsule through observation of the S21vector at a set of on-body receivers. Two methodologies i.e. complex cross-correlation and Minkowski distance were used to assess capsule orientation estimation with respect to a reference position. The study was performed within the Ultra-Wide Band frequency range as this technology is considered to be an attractive candidate for the next generation of wireless capsule endoscopy.

Lack of network availability or limited access to communication services are among the challenges that public safety officials and first responders could face during disasters. Networking infrastructure can partially (or sometimes fully) breakdown during a catastrophe. At the same time, unusual peaks in traffic load could lead to much higher blocking probability for critical communication. A possible solution for such scenarios is through the use of mobile infrastructures commonly referred to as Cells on Wheels (COW) or Cells on Light Trucks (COLT). These mobile cells can effectively complement the existing undamaged infrastructure or enable a temporary emergency network by themselves. Given the limited capacity of each cell, variable and spatially non-uniform traffic across the disaster area can make a big impact on the network performance. Not only judicious deployment of the cells can help to meet the coverage and capacity demands across the area, but also intelligent relocation strategies can optimally match the network resources to potentially changing traffic demands. Assuming that each cell can autonomously change its location, in this paper, we propose a decentralized relocation algorithm that adapts network coverage in order to increase the supported users traffic.

Public safety organizations increasingly rely on wireless technology for their mission critical communication during disaster response operations. In such situations, a communication network could face much higher traffic demands compared to its normal operation. Given the limited capacity of base stations in the network, such peak traffic scenarios could lead to high blocking probability or equivalently service interruptions during critical communications. At the same time, networking infrastructure can breakdown during a disaster. Proper deployment of mobile cells - Cells on Wheels - can help to enhance the network coverage or accommodate excess traffic in areas with high concentration of users. In addition, an intelligent relocation strategy can be used to efficiently adapt the cell locations to match variations in the spatial distribution of the traffic. In practical scenarios, these mobile base stations may not be able to relocate to all positions within the target field. Such prohibited areas introduce additional constraints on designing an intelligent relocation strategy. In this paper, we propose a decentralized relocation algorithm that enables mobile cells to adapt their positions in response to potentially changing traffic patterns in a field with prohibited areas. Extensive simulations show considerable improvement in supporting spatially variable traffic throughout the target field.

A Body Area Network (BAN) is a wireless protocol for connectivity of wearable and implantable sensors located inside, on the surface or near the human body. Medical applications requirements impose stringent constraints on the reliability, and quality of service (QoS) performance in these networks. Interference from other co-located BANs or nearby devices that share the same spectrum could greatly impact the communication link reliability in these networks. Link adaptation (LA) schemes can be an efficient alternative to preserve link quality in high interference environments. This paper proposes a low complexity link adaptation strategy to mitigate cross-interference in scenarios where multiple BANs are operating adjacent to each other. Each BAN is assumed to be using the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol as outlined by the IEEE 802.15.6 Standard, where different modulation schemes are available at the physical layer. Each node selects the appropriate modulation scheme based on the experienced channel quality indicated by the received Signal-to-Interference and Noise Ratio (SINR). System performance is evaluated in terms of Packet Delivery Ratio (PDR) per link. Simulation results demonstrate significant improvement in the performance and highlight potential benefits of using link adaptation schemes for BAN applications.

In this paper, an energy-efficient strategy is proposed for tracking a moving target in an environment with obstacles, using a network of mobile sensors. Typically, the most dominant sources of energy consumption in a mobile sensor network are sensing, communication, and movement. The proposed algorithm first divides the field into a grid of sufficiently small cells. The grid is then represented by a graph whose edges are properly weighted to reflect the energy consumption of sensors. The proposed technique searches for near-optimal locations for the sensors in different time instants to route information from the target to destination, using a shortest path algorithm. Simulations confirm the efficacy of the proposed algorithm.

IEEE802.15.6 is a radio interface standard for wireless connectivity of wearable and implantable sensors and actuators located inside or in close proximity to the human body i.e., Body Area Network (BAN). Medical applications impose stringent requirements on BAN Quality of Service (QoS), including reliability and on-time availability of data. However, interference from other co-located BANs or other nearby devices sharing the same spectrum, e.g., due to BAN mobility, may cause unacceptable QoS degradation. This paper suggests that the impact of such QoS degradations can be minimized with a queue-size and channel quality based adaptation of the Energy Detection Threshold (EDT) at the transmitting nodes. Guided by known results for Q-CSMA/CA, we propose an adaptive EDT algorithm for use in the IEEE 802.15.6 BAN standard. Our preliminary simulation results demonstrate the performance gain of our algorithm compared to using a fixed EDT, and thus warrant future efforts in the adaptive EDT optimization as a mechanism to maintain QoS in various interference scenarios.

IEEE 802.15.6 is a radio interface standard for a wireless connectivity of wearable and implantable sensors and actuators located inside or in close proximity to the human body i.e. Body Area Network (BAN). Medical applications requirements impose stringent constraints on the reliability, and quality of service performance in these networks. Assuming CSMA/CA MAC protocol as outlined in the IEEE 802.15.6 standard Intra-BAN interference as well as interference from other co-located BANs or nearby devices sharing the same spectrum could greatly impact the data link reliability in these networks. Specifically, inter-BAN interference caused by hidden and exposed nodes could lead to higher packet delays or lower successful packet reception. In this paper, we study the use of a RTS/CTS handshake mechanism to improve the performance of IEEE 802.15.6-based CSMA/CA when multiple co-located BANs are present. Simulation results demonstrate that the introduction of RTS/CTS mechanism is able to significantly reduce packet collisions, resulting in a tangible improvement in system performance.

Mobile sensor networking technology has attracted considerable attention in various research communities in recent years due to their widespread applications in civilian and military environments. One objective when using mobile sensors is to obtain maximum field coverage by properly deploying sensor nodes. In many real-world applications a priori knowledge about the best deployment position for the sensors is not available. However, the motion capability of the sensors could allow each node to adjust its position (i.e. relocate) so that a better (and ultimately maximal) coverage is achieved. In this paper, a novel autonomous joint sensing range and relocation control algorithm is presented that achieves improved coverage and network lifetime at the same time. In the proposed algorithm, the sensing range of each sensor is adjusted iteratively based on its residual energy. At the same time, the sensor is directed to move within its corresponding multiplicatively weighted Voronoi (MW-Voronoi) region to ultimately increase sensing coverage in the field. Simulation results demonstrate the efficacy of the technique.

A Body Area Network (BAN) is a radio standard for wireless connectivity of wearable and implantable sensor nodes that are located inside or in proximity to the human body. Many applications of BANs (e.g. physiological monitoring) require reliable communication of information between the sensor nodes and their controller. As there are currently no coordinating mechanisms among multiple co-located BANs, interference caused by co-channel transmission in adjacent BANs could impact the reliability and in general quality of the service experienced by a receiver node within an individual BAN. Here, we present a simulation platform that allows for statistical evaluation of interference in multi-BAN scenarios and performance of possible mitigation algorithms. Currently, there are no mechanisms for interfering BANs to explicitly coordinate their transmissions. As our analysis show, this may result in unacceptably high interference; and therefore, high link outage probability by the intended receiver. We propose uncoordinated approaches that could help to ease cross-interference among multiple adjacent BANs. Simulation results in our preliminary studies support the effectiveness of our approach.

In this paper, efficient sensor deployment strategies are developed to increase coverage in wireless mobile sensor networks. The sensors find coverage holes within their Voronoi polygons and then move in an appropriate direction to minimize them. Novel edge-based and vertex-based strategies are introduced, and their performances are compared with existing techniques. The proposed movement strategies are based on the distances of each sensor and the points inside its Voronoi polygon from the edges or vertices of the polygon. Simulations confirm the effectiveness of the proposed deployment algorithms and their superiority to the techniques reported in the literature.

A wireless Body Area Network (BAN) consists of multiple radio-enabled wearable and implantable sensor nodes for use inside or in proximity to the human body. BANs are expected to balance requirement for reliable communication of their nodes with a need for battery energy conservation to ensure their longevity. The main challenge in managing this tradeoff is the inability of interfering BANs to explicitly coordinate their transmissions. This may result in unacceptably high battery energy draining rates to overcome cross-interference by other BANs in the vicinity. Here, we propose a utility-based, link-layer adaptation framework for balancing these tradeoffs for each BAN as well as across different BANs. The framework accounts for the utility of the BANs data rates and penalties associated with high transmission powers. Our analysis indicates that link-layer adaptation is beneficial for mitigating the interference from multiple adjacent BANs. Simulation results support our mathematical framework, indicating a much better spectral efficiency obtainable by using link layer adaptation techniques.

Efficient deployment strategies are proposed for a mobile sensor network, where the coverage priority of different points in the field is specified by a given function. The multiplicatively weighted Voronoi (MW-Voronoi) diagram is utilized to find the coverage holes of the network for the case where the sensing ranges of different sensors are not the same. Under the proposed strategies, each sensor detects coverage holes within its MW-Voronoi region, and then moves in a proper direction to reduce their size. Since the coverage priority of the field is not uniform, the target location of each sensor is determined based on the weights of the vertices or the points inside the corresponding MW-Voronoi region. Simulations validate the theoretical results.

Efficient deployment strategies are proposed for a mobile sensor network, where the coverage priority of different points in the field is specified by a prescribed weight function. According to these strategies, each sensor detects coverage holes within its Voronoi polygon, and then moves in a proper direction to reduce them. Since the coverage priority of different points in the field is not the same, the target location of each sensor is determined based on the weights of the points inside the corresponding Voronoi polygon (or its vertices). Simulations confirm the theoretical results.

Due to very low power communication, wireless body area networks are potentially susceptible to interference from other coexisting wireless systems including other BANs that might exist in their vicinity. Using power control to combat this interference might not be efficient. It could also lead to situations with higher levels of interference in the system. On the other hand, interference mitigation can help to not only preserve link quality but also allow more nodes to simultaneously operate. This paper proposes several interference mitigation schemes such as adaptive modulation as well as adaptive data rate and duty cycle for body area networks. Interference Mitigation Factor (IMF) is introduced as a measure to quantify the effectiveness of the proposed schemes.

This paper reports on work in progress on developing a unified co-evolutionary/pricing framework for Mobile Sensor Networks (MSN) self-organization. MSN self-organization involves cooperative sensor positioning and formation of a multi-hop Mobile Ad-hoc NETwork (MANET) enabling sensor information acquisition and communication while prolonging MSN life-span. A number of inherent MSN traits such as lack of centralized control and variety of performance criteria suggest framing MSN self-organization as a sensor co-evolutionary optimization. The first issue to be addressed is aligning individual sensor utility/fitness landscapes with the overall MSN goals as “selfish” sensor behavior may result in suboptimal overall MSN performance. The paper proposes using “socially optimal pricing” to internalize the effect of each sensor relocation on the overall MSN performance. It is assumed that intermediate nodes in the MSN relay sensor information to a single fusion point without processing. The proposed framework is illustrated for the special case of a MSN tracking a single target.

In this paper, efficient sensor deployment algorithms are proposed to improve the coverage area in the target field. The proposed algorithms calculate the position of the sensors iteratively, based on the existing coverage holes in the target field. The multiplicatively weighted Voronoi (MW-Voronoi) diagram is used to discover the coverage holes corresponding to different sensors with different sensing ranges. Under the proposed procedures, the sensors move in such a way that the coverage holes in the target field are reduced. Simulation results are provided to demonstrate the effectiveness of the deployment schemes proposed in this paper.

A low complexity Parallel Interference Cancellation (PIC) technique is proposed to suppress Multi-Access Interference (MAI) and minimize near-far effect for multi-user communication using Direct-Sequence Code Division Multiple Access (DS-CDMA). Interference signal is estimated using a relationship between cyclic correlation of the received signal and user's code of the interferer without requiring any knowledge of the channel condition. The user's codes for the multi-user DS-CDMA communication are constructed by using a set of m-sequences. The cyclic correlation is performed using Fast Walsh-Hadamard Transform (FWHT) which exhibits low computational complexity, i.e., it does not require any multiplication/division operations. In addition to low complexity, the proposed technique does not require complicated channel condition estimation and has no convergence issues. The uncoded Bit Error Rate (BER) performance of the proposed interference cancellation is calculated and compared with the conventional one over three different types of channels: AWGN, slow Rayleigh fading, and multipath channels.

A low complexity Parallel Interference Cancellation (PIC) technique that is applicable to body area networks (BANs) is presented. Using Direct-Sequence Code Division Multiple Access (DS-CDMA), the technique aims at suppressing interference caused by rapid changes in relative sensors position due to body parts motion as well as interference from adjacent BANs. Interference signal is estimated using a relationship between cyclic correlation of the received signal and interferer code without requiring any knowledge of the channel condition. The codes for the multi-sensor DS-CDMA communication are constructed by using a set of m-sequences. The cyclic correlation is performed using Fast Walsh-Hadamard Transform (FWHT) which exhibits low computational complexity. In addition to low complexity, the proposed technique does not require complicated channel condition estimation and has no convergence issues. The uncoded Bit Error Rate (BER) performance of the proposed interference cancellation over a body-surface channel is calculated and compared with the conventional scheme.

Energy Harvesting (EH) refers to the process of capturing and storing energy from external sources or ambient environment. Kinetic energy harvested from the human body motion seems to be one of the most convenient and attractive solution for wearable wireless sensors in healthcare applications. Due to their small size, such sensors have a very limited battery power supply, which necessitates frequent recharge or even sensor replacement. Energy harvesting can prolong the battery lifetime of these sensors. This could directly impact their everyday use and significantly help their commercial applications such as remote monitoring. In this paper, our aim is to estimate the amount of harvestable energy from typical human motion. To simplify the measurement process, we focus on the amount of kinetic energy harvested from the human forearm motion. We provide statistical analysis of measurements taken from 40 test subjects over a period of 8 hours during the day. Using this information and knowing the operational architecture of the harvesting device, the distribution of harvestable energy can also be determined. Our objective is to study whether kinetic energy generated by typical human forearm motion could be a promising supplemental energy resource that prolongs the operational lifetime of wearable medical sensors.

Knowledge of the propagation media is a key step toward a successful transceiver design. Such information is typically gathered by conducting physical experiments, measuring and processing the corresponding data to obtain channel characteristics. In case of medical implants, this could be extremely difficult, if not impossible. In this paper, an immersive visualization environment is presented, which is used as a scientific instrument that gives us the ability to observe RF propagation from medical implants inside a human body. This virtual environment allows for more natural interaction between experts with different backgrounds, such as engineering and medical sciences. Here, we show how this platform has been used to determine a statistical path loss model for medical implant communication systems.

A simple technique to estimate the position of a mobile node inside a building is based on the Received Signal Strength (RSS). In previous publications, we investigated the effectiveness of using circular array antennas and beamforming in order to enable an access point to estimate the position of a mobile inside a building. We also discussed the feasibility of using model-based radio maps to reduce the need for extensive offline measurements. In this paper, a positioning algorithm based on the relative order of the received signal strengths is discussed. This algorithm in conjunction with the ray-tracing propagation model can have promising performance for indoor environments and essentially eliminates the needs for an extensive set of a priori measurement, training or intricate calibration.

Temporal-Angular channel sounding measurements of an indoor millimeter wave channel (60 GHz) is analyzed to determine whether ray arrivals at the receiver form clusters in the two-dimensional time-angle space. The channel behavior and cluster energy distribution for directional antennas (with various 3 dB beamwidths) at the transmitter are discussed. Geometric interpretation of the results is also provided.

Temporal-Angular channel sounding measurements of an indoor millimeter wave channel (60 GHz) is analyzed to determine the location of two dimensional clusters of arrivals at the receiver. The measurement scenarios are also emulated by a ray tracing tool. The results are similarly analyzed to verify possible agreements and determine the effectiveness of such tools in predicting cluster locations as well as ray arrival statistics within clusters in millimeter wave indoor channel.

A simple technique to estimate the position of a mobile node inside a building is based on the received signal strength (RSS). In a previous publication, we investigated the feasibility of using circular array antennas and beamforming in order to enable an access point to estimate the position of a mobile inside a building. The approach utilized the two dimensional information (i.e. RSS for various azimuth directions) that is captured in a priori measured radio map. Generating these radio maps is not only extremely labor- intensive and time consuming but also sensitive to changes in the environment and possible source of interference. It would be interesting to find out if a deterministic propagation model such as ray tracing can be used to construct a radio map that effectively replaces the off-line manual measurements. In this paper, we investigate this issue and provide a novel positioning methodology that exhibits acceptable performance without the need for extensive set of measurements in the off-line mode. The performance for various parameters and building model accuracy will be presented and discussed.

Abstract — Direction estimation inside buildings is a difficult and challenging task due to severe multipath signal propagation. Numerous algorithms and techniques exist that provide highresolution direction estimation under certain conditions and channel models; however, to our knowledge they all perform poorly at indoor environments. Here, we propose a technique that enables a receiver to achieve greater reliability in estimating source direction through some collaboration with the source. We assume that the receiver and the transmitter are synchronized and they are equipped with circular phased array antennas that have beamforming capability. If the transmitter-receiver pair always steer their main lobes into opposite directions, the spatial spectrum of the received power can be used as a mean for estimating the direction of the transmitting source. In this paper, we investigate the feasibility of this methodology, and show the achieved improvement. I.

A simple technique to estimate the position of a given mobile source inside a building is based on the received signal strength. For this methodology to have a reasonable accuracy, radio visibility of the mobile by at least three access points is required. To reduce the number of the required access points and therefore, simplify the underlying coverage design problem, we propose a new scheme that takes into account the distribution of RF energy around the receiver. In other words, we assume that the receiver is equipped with a circular antenna with beamforming capability. In this way, the spatial spectrum of the received power can be measured by rotating the antenna beam around the 360-degree field of view. This spatial spectrum can be used by a single receiver as a mean for estimating the position of a mobile transmitter. In this paper, we investigate the feasibility of this methodology, and show the improvement achieved in the positioning accuracy

Suppose that the description of a stochastic process needs to be sent to a destination through a communication network. Also assume there is a risk that the description may be lost. A technique to reduce the risk of losing such descriptions is by sending two (or more) descriptions and hoping that in this way at least one of the descriptions will get through. This problem is referred to as multiple description coding (MDC) and was first introduced by Gersho, Witsenhausen, Wolf, Wyner, Ziv and Ozarow (1979). So far, the main focus of research on this subject has been on the achievable rate-distortion functions and the related structural design issues for such encoders and decoders. Little effort has focused on the performance of such coding schemes in simple communication networks and relation of the overall distortion with respect to some network parameter such as congestion. In this paper, a double description coding (DDC) system in a simple network represented by a set of parallel queues is studied. Comparison is made with a single description coding system and it is shown that DDC significantly improves the overall average end-to-end distortion at high network loading.

We consider multihop packet radio networks that need to determine broadcasting transmission schedules. To provide an opportunity for increasing system throughput, it is assumed that multiple radio channels are available for exchanging messages. This multiple reception capacity of each node presents a challenging problem for channel access protocols. Variations of this problem under some restrictive assumptions have been investigated in the literature. In this paper a time-frequency scheduling algorithm is proposed that guarantees a collision-free broadcast traffic flow and ensures high slot utilization. An efficient distributed scheduling algorithm is also described for such multi-channel multi-hop packet radio networks.