Scalability is a major issue for Internet of Things (IoT) as the total amount of traffic data collected and or the number of sensors deployed grow. In some IoT applications such as healthcare, power consumption is also a key design factor for the IoT devices. In this paper, a multi-signal compression and encoding method based on Analog Joint Source Channel Coding (AJSCC) is proposed that works fully in the analog domain without the need for power-hungry Analog-to-Digital Converters (ADCs). Compression is achieved by quantizing all the input signals but one. While saving power, this method can also reduce the number of devices by combining one or more sensing functionalities into a single device (called 'AJSCC device'). Apart from analog encoding, AJSCC devices communicate to an aggregator node (FPMM receiver) using a novel Frequency Position Modulation and Multiplexing (FPMM) technique. Such joint modulation and multiplexing technique presents three mayor advantages&#x2014;it is robust to interference at particular frequency bands, it protects against eavesdropping, and it consumes low power due to a very low Signal-to-Noise Ratio (SNR) operating region at the receiver. Performance of the proposed multi-signal compression method and FPMM technique is evaluated via simulations in terms of Mean Square Error (MSE) and Miss Detection Rate (MDR), respectively.
In the IoT healthcare and BAN domain, all of the existing solutions do sensing and communication in the digital domain (using ADCs DACs Microprocessors), which needs more power than analog sensing and communication. For example, Yang's group @cite_18 developed a miniaturized node that incorporates wireless communication, on-board processing, nine-axis motion tracking, and other sensors @cite_0 . It also developed an e-AR sensor @cite_27 , a small device to be worn behind the ear that captures information about the balance of the wearer such as gait, posture, skelet al joint shock-wave transmission, and activity of the individual. Yuce's group developed techniques based on Ultra Wide Band (UWB) wireless technology to reduce the power consumption of body-worn sensors @cite_11 @cite_8 . Another important example is the activity recognition of the user using various body sensors viz., accelerometer and gyroscope data. The data from the sensors is digitally processed and stored into a wrist-band device that syncs the data with the mobile phone using Bluetooth technology @cite_19 . Unlike these approaches, we adopt an entirely different approach based on analog sensing and communication that does not use any power-hungry ADCs (see Table ).
@cite_18 @cite_8 @cite_0 @cite_19 @cite_27 @cite_11
95821903 2143221170 2520465429 1940704660 2042956874
The basic requirement of wireless healthcare monitoring systems is to send physiological signals acquired from implantable or on-body sensor nodes to a remote location. Low-power consumption is required for wireless healthcare monitoring systems since most medical sensor nodes are battery powered. The emergence of new technologies in measuring physiological signals has increased the demand for high data rate transmission systems. Ultra-wide band (UWB) is a suitable wireless technology to achieve high data rates while keeping power consumption and form factors small. Although UWB transmitters are designed based on simple techniques, UWB receivers require complex hardware and consume comparatively higher power. In order to achieve reliable low power two-way communication, a sensor node can be constructed using a UWB transmitter and a narrow band receiver. This paper proposes a new medium access control (MAC) protocol based on a dual-band physical layer technology. Co-simulation models based on MATLAB and OPNET have been developed to analyze the performance of the proposed MAC protocol. We analyzed the performance of the MAC protocol for a realistic scenario where both implantable and wearable sensor nodes are involved in the data transmission. Priority-based packet transmission techniques have been used in the MAC protocol to serve different sensors according to their QoS requirements. Analysis is done with regard to important network parameters, such as packet loss ratio, packet delay, percentage throughput, and power consumption. Advances in wireless communication technologies, such as wearable and implantable biosensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of innovative healthcare monitoring applications. In the past few years, much of the research in the area of body area networks has focused on issues related to wireless sensor designs, sensor miniaturization, low-power sensor circuitry, signal processing, and communications protocols. In this paper, we present an overview of body area networks, and a discussion of BAN communications types and their related issues. We provide a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research. We also present a taxonomy of BAN projects that have been introduced proposed to date. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make BANs truly ubiquitous for a wide range of applications. The continual advancement in semiconductor technology has enabled vendors to integrate an increasing number of sensors, e.g., accelerometer, gyroscope, digital compass, high resolution cameras and others, in modern mobile phones. Combining with the ever expanding market penetration of mobile phones, the onboard sensors are driving the sensing community towards a new paradigm called participatory sensing in which ordinary citizens voluntarily collect and share information from their local environment using personal mobile devices. The inherent openness of this platform is a double-edged sword. While encouraging user participation on the one hand, it also makes it easy to contribute corrupted data. As such, data trustworthiness becomes a key issue which needs to be addressed to ensure sustainable development of this emerging paradigm. In this paper, we propose a reputation system in which a reputation score is calculated for each device as a reflection of the trustworthiness of its sensor data. We adopt the Gompertz function as a fundamental building block of our system, since it is better suited to deal with the unique characteristics of participatory sensing. We present an application-agnostic implementation of the system, which can work with a wide variety of participatory sensing applications. We evaluate the performance of our reputation system in the context of two real-world participatory sensing applications. Our results show that the proposed reputation system outperforms existing solutions by up to a factor of six. As the proliferation of pervasive and ubiquitous computing devices continues, users will carry more devices. Without the ability for these devices to unobtrusively interact with one another, the user’s attention will be spent on coordinating, rather than using, these devices. We present a method to determine if two devices are carried by the same person, by analyzing walking data recorded by low-cost MEMS accelerometers using the coherence function, a measure of linear correlation in the frequency domain. We also show that these low-cost sensors perform similarly to more expensive accelerometers for the frequency range of human motion, 0 to 10Hz. We also present results from a large test group illustrating the algorithm’s robustness and its ability to withstand real world time delays, crucial for wireless technologies like Bluetooth and 802.11. We present results that show that our technique is 100 accurate using a sliding window of 8 seconds of data when the devices are carried in the same location on the body, is tolerant to inter-device communication latencies, and requires little communication bandwidth. In addition we present results for when devices are carried on different parts of the body. Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors. These sensors include GPS sensors, vision sensors (i.e., cameras), audio sensors (i.e., microphones), light sensors, temperature sensors, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., accelerometers). The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing. To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets. Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (e.g., sending calls directly to voicemail if a user is jogging) and generating a daily weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise. The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2–30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available. Wireless sensor networks provide an attractive approach to spatially monitoring environments. Wireless technology makes these systems relatively flexible, but also places heavy demands on energy consumption for communications. This raises a fundamental trade-off: using higher densities of sensors provides more measurements, higher resolution and better accuracy, but requires more communications and processing. This paper proposes a new approach, called "back-casting," which can significantly reduce communications and energy consumption while maintaining high accuracy. Back-casting operates by first having a small subset of the wireless sensors communicate their information to a fusion center. This provides an initial estimate of the environment being sensed, and guides the allocation of additional network resources. Specifically, the fusion center backcasts information based on the initial estimate to the network at large, selectively activating additional sensor nodes in order to achieve a target error level. The key idea is that the initial estimate can detect correlations in the environment, indicating that many sensors may not need to be activated by the fusion center. Thus, adaptive sampling can save energy compared to dense, non-adaptive sampling. This method is theoretically analyzed in the context of field estimation and it is shown that the energy savings can be quite significant compared to conventional approaches. For example, when sensing a piecewise smooth field with an array of 100 spl times 100 sensors, adaptive sampling can reduce the energy consumption by roughly a factor of 10 while providing the same accuracy achievable if all sensors were activated. A Wireless Body Area Network (WBAN) consists of different tiny physiological wearable sensors to monitor the vital signs of a human body and these sensor nodes transmit in real-time the sensed physiological information to the on-body coordinator or server over a wireless medium. Any radio frequency based wireless device suffers from interference due to the existence of other wireless devices operating in the same frequency band. In this paper we address the problem of interference when multiple WBANs come in the proximity of one another. We propose a TDMA-based solution that creates a common schedule between these WBANs, so that there is seamless communication with their sensors without interference among them, while increasing the cost of latency per node. Also, when these TDMA based WBANs come in the close proximity of one another, they sense the existence of other interfering WBANs. Our solution proposed a scheme that defines the time when to exchange their TDMA schedules. We simulated the proposed solution using NS-3 network simulator and it is observed that there is improvement in the percentage of packet delivery by using the proposed scheme. It is also observed that the increase of speed of WBANs leads to decrease in the percentage of packet delivery. The wireless body area network has emerged as a new technology for e-healthcare that allows the data of a patient's vital body parameters and movements to be collected by small wearable or implantable sensors and communicated using short-range wireless communication techniques. WBAN has shown great potential in improving healthcare quality, and thus has found a wide range of applications from ubiquitous health monitoring and computer assisted rehabilitation to emergency medical response systems. The security and privacy protection of the data collected from a WBAN, either while stored inside the WBAN or during their transmission outside of the WBAN, is a major unsolved concern, with challenges coming from stringent resource constraints of WBAN devices, and the high demand for both security privacy and practicality usability. In this article we look into two important data security issues: secure and dependable distributed data storage, and fine-grained distributed data access control for sensitive and private patient medical data. We discuss various practical issues that need to be taken into account while fulfilling the security and privacy requirements. Relevant solutions in sensor networks and WBANs are surveyed, and their applicability is analyzed. Sensor-equipped smartphones and wearables are transforming a variety of mobile apps ranging from health monitoring to digital assistants. However, reliably inferring user behavior and context from noisy and complex sensor data collected under mobile device constraints remains an open problem, and a key bottleneck to sensor app development. In recent years, advances in the field of deep learning have resulted in nearly unprecedented gains in related inference tasks such as speech and object recognition. However, although mobile sensing shares many of the same data modeling challenges, we have yet to see deep learning be systematically studied within the sensing domain. If deep learning could lead to significantly more robust and efficient mobile sensor inference it would revolutionize the field by rapidly expanding the number of sensor apps ready for mainstream usage. In this paper, we provide preliminary answers to this potentially game-changing question by prototyping a low-power Deep Neural Network (DNN) inference engine that exploits both the CPU and DSP of a mobile device SoC. We use this engine to study typical mobile sensing tasks (e.g., activity recognition) using DNNs, and compare results to learning techniques in more common usage. Our early findings provide illustrative examples of DNN usage that do not overburden modern mobile hardware, while also indicating how they can improve inference accuracy. Moreover, we show DNNs can gracefully scale to larger numbers of inference classes and can be flexibly partitioned across mobile and remote resources. Collectively, these results highlight the critical need for further exploration as to how the field of mobile sensing can best make use of advances in deep learning towards robust and efficient sensor inference. Consumer-grade, real-time motion capture devices are becoming commonplace in every household, thanks to the recent development in depth-camera technologies. We introduce a new approach to capturing and reconstructing freeform, full-body human motion using force sensors, supplementary to existing, consumer-grade mocap systems. Our algorithm exploits the dynamic aspects of human movement, such as linear and angular momentum, to provide key information for full-body motion reconstruction. Using two pressure sensing platforms (Wii Balance Board) and a hand tracking device, we demonstrate that human motion can be largely reconstructed from ground reaction forces along with a small amount of arm movement information. Environmental Sensor Networks (ESNs) facilitate the study of fundamental processes and the development of hazard response systems. They have evolved from passive logging systems that require manual downloading, into ‘intelligent’ sensor networks that comprise a network of automatic sensor nodes and communications systems which actively communicate their data to a Sensor Network Server (SNS) where these data can be integrated with other environmental datasets. The sensor nodes can be fixed or mobile and range in scale appropriate to the environment being sensed. ESNs range in scale and function and we have reviewed over 50 representative examples. Large Scale Single Function Networks tend to use large single purpose nodes to cover a wide geographical area. Localised Multifunction Sensor Networks typically monitor a small area in more detail, often with wireless adhoc systems. Biosensor Networks use emerging biotechnologies to monitor environmental processes as well as developing proxies for immediate use. In the future, sensor networks will integrate these three elements (Heterogeneous Sensor Networks). The communications system and data storage and integration (cyberinfrastructure) aspects of ESNs are discussed, along with current challenges which need to be addressed. We argue that Environmental Sensor Networks will become a standard research tool for future Earth System and Environmental Science. Not only do they provide a ‘virtual’ connection with the environment, they allow new field and conceptual approaches to the study of environmental processes to be developed. We suggest that although technological advances have facilitated these changes, it is vital that Earth Systems and Environmental Scientists utilise them. As the domains of pervasive computing and sensor networking are expanding, there is an ongoing trend towards assistive living and healthcare support environments that can effectively assimilate these technologies according to human needs. Most of the existing research in assistive healthcare follows a more passive approach and has focused on collecting and processing data using a static-topology and an application-aware infrastructure. However, with the technological advances in sensing, computation, storage, and communications, a new era is about to emerge changing the traditional view of sensor-based assistive environments where people are passive data consumers, with one where people carry mobile sensing elements involving large volumes of data related to everyday human activities. This evolution will be driven by people-centric sensing and will turn mobile phones into global mobile sensing devices enabling thousands new personal, social, and public sensing applications. In this paper, we discuss our vision for people-centric sensing in assistive healthcare environments and study the security challenges it brings. This highly dynamic and mobile setting presents new challenges for information security, data privacy and ethics, caused by the ubiquitous nature of data traces originating from sensors carried by people. We aim to instigate discussion on these critical issues because people-centric sensing will never succeed without adequate provisions on security and privacy. To that end, we discuss the latest advances in security and privacy protection strategies that hold promise in this new exciting paradigm. We hope this work will better highlight the need for privacy in people-centric sensing applications and spawn further research in this area. Copyright © 2011 John Wiley & Sons, Ltd. The increasing use of wireless networks and the constant miniaturization of electrical devices has empowered the development of Wireless Body Area Networks (WBANs). In these networks various sensors are attached on clothing or on the body or even implanted under the skin. The wireless nature of the network and the wide variety of sensors offer numerous new, practical and innovative applications to improve health care and the Quality of Life. The sensors of a WBAN measure for example the heartbeat, the body temperature or record a prolonged electrocardiogram. Using a WBAN, the patient experiences a greater physical mobility and is no longer compelled to stay in the hospital. This paper offers a survey of the concept of Wireless Body Area Networks. First, we focus on some applications with special interest in patient monitoring. Then the communication in a WBAN and its positioning between the different technologies is discussed. An overview of the current research on the physical layer, existing MAC and network protocols is given. Further, cross layer and quality of service is discussed. As WBANs are placed on the human body and often transport private data, security is also considered. An overview of current and past projects is given. Finally, the open research issues and challenges are pointed out. To enable precise detection of mental and physical states of users in a daily life, we have been developing an eyewear to measure eye and body movement in a unrestricted way. The horizontal and vertical EOG (electrooculogram) signals are measured and amplified with three met al dry electrodes placed near nasion and both sides of rhinion, of which positions correspond to the bridge and nose pads of eyewear, respectively. The user's mental states like drowsiness, sleepiness, fatigue, or interest to objects can be identified by the movements and blinking of the eyes extracted from the measured EOG. And the six-axis motion sensor (three-axis accelerometer and three-axis gyroscope) mounted in the eyewear measures the body motion. As the sensor located near the head is on the body axis, this eyewear is suitable to measure user's movement or shift of center of gravity during physical exercise with a high precision. The measured signals are used to extract various events of eye and body movement by the mounted microcontroller chip, or can be transmitted to the external devices via Bluetooth communication. This device can enable you to look into "yourself", as well as outer scenes. In this presentation, the outline of the eyewear is introduced and some possible applications are shown. This paper presents MyExperience, a system for capturing both objective and subjective in situ data on mobile computing activities. MyExperience combines the following two techniques: 1) passive logging of device usage, user context, and environmental sensor readings, and 2) active context-triggered user experience sampling to collect in situ, subjective user feedback. MyExperience currently runs on mobile phones and supports logging of more than 140 event types, including: 1) device usage such as communication, application usage, and media capture, 2) user context such as calendar appointments, and 3) environmental sensing such as Bluetooth and GPS. In addition, user experience sampling can be targeted to moments of interest by triggering off sensor readings. We present several case studies of field deployments on people's personal phones to demonstrate how MyExperience can be used effectively to understand how people use and experience mobile technology. This paper develops an optimal decentralized algorithm for sparse signal recovery and demonstrates its application in monitoring localized phenomena using energy-constrained large-scale wireless sensor networks. Capitalizing on the spatial sparsity of localized phenomena, compressive data collection is enforced by turning off a fraction of sensors using a simple random node sleeping strategy, which conserves sensing energy and prolongs network lifetime. In the absence of a fusion center, sparse signal recovery via decentralized in-network processing is developed, based on a consensus optimization formulation and the alternating direction method of multipliers. In the proposed algorithm, each active sensor monitors and recovers its local region only, collaborates with its neighboring active sensors through low-power one-hop communication, and iteratively improves the local estimates until reaching the global optimum. Because each sensor monitors the local region rather than the entire large field, the iterative algorithm converges fast, in addition to being scalable in terms of transmission and computation costs. Further, through collaboration, the sensing performance is globally optimal and attains a high spatial resolution commensurate with the node density of the original network containing both active and inactive sensors. Simulations demonstrate the performance of the proposed approach. Wireless sensor networks have attracted a lot of attention recently. Such environments may consist of many inexpensive nodes, each capable of collecting, storing, and processing environmental information, and communicating with neighboring nodes through wireless links. For a sensor network to operate successfully, sensors must maintain both sensing coverage and network connectivity. This issue has been studied in [2003] and Zhang and Hou [2004a], both of which reach a similar conclusion that coverage can imply connectivity as long as sensors' communication ranges are no less than twice their sensing ranges. In this article, without relying on this strong assumption, we investigate the issue from a different angle and develop several necessary and sufficient conditions for ensuring coverage and connectivity of a sensor network. Hence, the results significantly generalize the results in [2003] and Zhang and Hou [2004a]. This work is also a significant extension of our earlier work [Huang and Tseng 2003; 2004], which addresses how to determine the level of coverage of a given sensor network but does not consider the network connectivity issue. Our work is the first work allowing an arbitrary relationship between sensing ranges and communication distances of sensor nodes. We develop decentralized solutions for determining, or even adjusting, the levels of coverage and connectivity of a given network. Adjusting levels of coverage and connectivity is necessary when sensors are overly deployed, and we approach this problem by putting sensors to sleep mode and tuning their transmission powers. This results in prolonged network lifetime. A wireless sensor network consists of many low-cost, low-power sensor nodes, which can perform sensing, simple computation, and transmission of sensed information. Long distance transmission by sensor nodes is not energy efficient since energy consumption is a superlinear function of the transmission distance. One approach to prolonging network lifetime while preserving network connectivity is to deploy a small number of costly, but more powerful, relay nodes whose main task is communication with other sensor or relay nodes. In this paper, we assume that sensor nodes have communication range r>0, while relay nodes have communication range Rgesr, and we study two versions of relay node placement problems. In the first version, we want to deploy the minimum number of relay nodes so that, between each pair of sensor nodes, there is a connecting path consisting of relay and or sensor nodes. In the second version, we want to deploy the minimum number of relay nodes so that, between each pair of sensor nodes, there is a connecting path consisting solely of relay nodes. We present a polynomial time 7-approximation algorithm for the first problem and a polynomial time (5+epsi)-approximation algorithm for the second problem, where epsi>0 can be any given constant Recent advances in electronics and wireless communication technologies have enabled the development of large-scale wireless sensor networks that consist of many low-power, low-cost, and small-size sensor nodes. Sensor networks hold the promise of facilitating large-scale and real-time data processing in complex environments. Security is critical for many sensor network applications, such as military target tracking and security monitoring. To provide security and privacy to small sensor nodes is challenging, due to the limited capabilities of sensor nodes in terms of computation, communication, memory storage, and energy supply. In this article we survey the state of the art in research on sensor network security.