In combination with current sociological trends, the maturing development of IoT devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data that is orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, HIoT data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this paper, we survey existing and emerging technologies that can enable this vision for the future of healthcare, particularly in the clinical practice of healthcare. Three main technology areas underlie the development of this field: (a) sensing, where there is an increased drive for miniaturization and power efficiency; (b) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure, and (c) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout the paper, we use a case study to concretely illustrate the impact of these trends. We conclude our paper with a discussion of the emerging directions, open issues, and challenges.

This paper proposes a new framework for emulating the functionality of a sensor by using multiple available soft sensors and machine intelligence algorithms. As a case study, the localization of city buses in a smart city setting is investigated by using the accelerometer and microphones of the passengers and a Support Vector Machine (SVM) running in the cloud; in this application, the GPS functionality is emulated by using these two soft sensors. What makes such an emulation feasible is the statistical dependence of the location data (which would normally be obtained from a GPS) on the accelerometer and microphone data; while accelerometers capture data that relate to the typical stop/start patterns of the buses, microphone capture enter/exit patterns of the passengers through the sound levels inside the bus. We evaluate our proposed scheme through simulations and show that the proposed framework can operate with more than 90% accuracy in estimating the location of public buses while preserving the actual location privacy of the smartphone users. This approach results in smartphone battery energy savings of 38-46% (as compared to GPS-based approaches) due to the elimination of the power-hungry GPS devices.

The past few decades have witnessed incredible advances in human health care, owing to the invention of devices such as MRI scanners, which allow physicians to monitor personal health in more detail than was ever previously possible. Such advances have drastically improved diagnostic quality and patient health care. Central to this incredible progress was the uncanny ability of technologists and academics to invent ever more useful tools to help physicians, be it the X-ray machine, CT, or MRI scanner. Whereas the aforementioned past-decades' tools aimed at acquiring personal data, the advent of the Internet-of-Things, vast computational power available in the cloud, and new data analytics algorithms will completely change the way we acquire and process medical data to improve health care going forward. In this paper, we conduct a quantitative feasibility study of a Digital Health (D-Health) system that is aimed at acquiring and processing health data using the emerging Internet-of-Everything paradigm. We specifically investigate the technological feasibility of communication, software, and data privacy aspects.

The following decade will witness a surge in remote health-monitoring systems that are based on body-worn monitoring devices. These Medical Cyber Physical Systems (MCPS) will be capable of transmitting the acquired data to a private or public cloud for storage and processing. Machine learning algorithms running in the cloud and processing this data can provide decision support to healthcare professionals. There is no doubt that the security and privacy of the medical data is one of the most important concerns in designing an MCPS. In this paper, we depict the general architecture of an MCPS consisting of four layers: data acquisition, data aggregation, cloud processing, and action. Due to the differences in hardware and communication capabilities of each layer, different encryption schemes must be used to guarantee data privacy within that layer. We survey conventional and emerging encryption schemes based on their ability to provide secure storage, data sharing, and secure computation. Our detailed experimental evaluation of each scheme shows that while the emerging encryption schemes enable exciting new features such as secure sharing and secure computation, they introduce several orders-of-magnitude computational and storage overhead. We conclude our paper by outlining future research directions to improve the usability of the emerging encryption schemes in an MCPS.

With a large number of commercially-available noninvasive health monitoring sensors today, remote health monitoring of patients in their homes is becoming widespread. In remote health monitoring, acquired sensory data is transferred into a private or public cloud for storage and processing. While simple encryption techniques can assure data privacy in the case of private clouds, ensuring data privacy becomes a lot more challenging when a public cloud (e.g., Amazon EC2) is used to store and process data. We present an approach that eliminates data privacy concerns in the public cloud scenario, by utilizing an emerging encryption technique called Fully Homomorphic Encryption (FHE). The ability of FHE to allow computations without actually observing the data itself makes it an attractive option for certain medical applications. In this paper, we use cardiac health monitoring for our feasibility assessment and demonstrate the advantages and challenges of our approach by utilizing a well-established FHE library called HElib.

As global healthcare systems transition into the digital era, remote patient health monitoring will be widespread through the use of inexpensive monitoring devices, such as ECG patches, glucose monitors, etc. Once a sensor-concentrator-cloudlet-cloud infrastructure is in place, it is not unrealistic to imagine a scenario where a physician monitors 20-30 patients remotely. Such an infrastructure will revolutionize clinical diagnostics and preventative medicine by allowing the doctors to access long-term and real-time information, which cannot be obtained from short-term in-hospital ECG recordings. While the large amount of sensor data available to a physician is incredibly valuable clinically, it is overwhelming in raw form. In this paper, the data handling aspect of such a long term health monitoring system is studied. Novel ways to record, aggregate, and visualize this flood of sensory data in an intuitive manner are introduced which allow a doctor to review days worth of data in a matter of seconds. This system is one of the first attempts to provide a tool that allows the visualization of long-term monitoring data acquired from multiple sensors.

As servers are placed in diverse locations in networked services today, it becomes vital to direct a client's request to the best server(s) to achieve both high performance and reliability. In this distributed setting, non-negligible latency and server availability become two major concerns, especially for highly-interactive applications. Profiling latencies and sending redundant data have been investigated as solutions to these issues. The notion of a cloudlet in mobile-cloud computing is also relevant in this context, as the cloudlet can supply these solution approaches on behalf of the mobile. In this paper, we investigate the effects of profiling and redundancy on latency when a client has a choice of multiple servers to connect to, using measurements from real experiments and simulations. We devise and test different server selection and data partitioning strategies in terms of profiling and redundancy. Our key findings are summarized as follows. First, intelligent server selection algorithms help find the optimal group of servers that minimize latency with profiling. Second, we can achieve good performance with relatively simple approaches using redundancy. Our analysis of profiling and redundancy provides insight to help designers determine how many servers and which servers to select to reduce latency.

Supercapacitors are an attractive option for energy buffering because of their high efficiency, durability, and low environmental impact. For energy-aware applications, it is desirable to accurately estimate the buffered energy. Under conditions of varying energy supply and demand, estimation of buffered energy by using only the supercapacitor terminal voltage is inaccurate because this does not fully comprehend the physical state of charge. To address this problem, we present a Kalman filtering formulation, using the accepted three-branch circuit model for supercapacitors. Compared with an ideal capacitor, the physically-motivated three-branch model provides a much more accurate representation of the state of charge via three internal state voltages associated with short, medium, and long term charging constants. The proposed Kalman formulation tracks these unobservable internal states. This methodology demonstrates a significantly more accurate estimate of the buffered energy as compared with the alternative models of ideal capacitance or a recursive computation of the stored energy. Simulations conducted with variations that approximate recorded solar intensity profiles, our proposed approach has an error of 1% compared with 31% and 85% for the respective alternative models.

Current financial and regulatory pressure has provided strong incentives to institute better disease prevention, improved patient monitoring, and push U.S. healthcare into the digital era. This transition requires that data privacy be ensured for digital health data in three distinct phases: I. acquisition, II. storage, and III. computation. Each phase comes with unique challenges in terms of proper implementation and privacy. While the privacy of the data can be ensured with existing AES encryption techniques in phases I (acquisition) and II (storage), to enable healthcare organizations to take advantage of cloud computing using resources such as Amazon Web Services, phase III (computation) must also enable the privacy of the data. Currently, there exists no system to enable direct computation in the cloud while assuring data privacy. Fully Homomorphic Encryption (FHE) is an emerging cryptographic technique to permit computation on encrypted data directly in the cloud without the need to bring the data back to the computational node. However, this promising technique comes with significant performance- and storage-related challenges. While it will take more years before true FHE is mainstream, we provide a feasibility study for its application to a simple longterm patient ECG-data monitoring system.

Face recognition applications for airport security and surveillance can benefit from the collaborative coupling of mobile and cloud computing as they become widely available today. This paper discusses our work with the design and implementation of face recognition applications using our mobile-cloudlet-cloud architecture named MOCHA and its initial performance results. The challenge lies with how to perform task partitioning from mobile devices to cloud and distribute compute load among cloud servers (cloudlet) to minimize the response time given diverse communication latencies and server compute powers. Our preliminary simulation results show that optimal task partitioning algorithms significantly affect response time with heterogeneous latencies and compute powers. Motivated by these results, we design, implement, and validate the basic functionalities of MOCHA as a proof-of-concept, and develop algorithms that minimize the overall response time for face recognition. Our experimental results demonstrate that high-powered cloudlets are technically feasible and indeed help reduce overall processing time when face recognition applications run on mobile devices using the cloud as the backend servers.

As CMOS scales beyond the 45nm technology node, leakage concerns are starting to limit microprocessor performance growth. To keep dynamic power constant across process generations, traditional MOSFET scaling theory prescribes reducing supply and threshold voltages in proportion to device dimensions, a practice that induces an exponential increase in subthreshold leakage. As a result, leakage power has become comparable to dynamic power in current-generation processes, and will soon exceed it in magnitude if voltages are scaled down any further. Beyond this inflection point, multicore processors will not be able to afford keeping more than a small fraction of all cores active at any given moment. Multicore scaling will soon hit a power wall. This paper presents resistive computation, a new technique that aims at avoiding the power wall by migrating most of the functionality of a modern microprocessor from CMOS to spin-torque transfer magnetoresistive RAM (STT-MRAM)---a CMOS-compatible, leakage-resistant, non-volatile resistive memory technology. By implementing much of the on-chip storage and combinational logic using leakage-resistant, scalable RAM blocks and lookup tables, and by carefully re-architecting the pipeline, an STT-MRAM based implementation of an eight-core Sun Niagara-like CMT processor reduces chip-wide power dissipation by 1.7× and leakage power by 2.1× at the 32nm technology node, while maintaining 93% of the system throughput of a CMOS-based design.

A retiming algorithm is presented which includes the effects of variable register, clock distribution, and interconnect delays. These delay components are incorporated into the retiming process by assigning register electrical characteristics (RECs) to each edge in the graph representation of a synchronous circuit. A matrix, called the sequential adjacency matrix (SAM), is presented that contains all path delays. Timing constraints for each data path are derived from this matrix. Vertex lags are assigned ranges rather than single values as in existing retiming algorithms. The approach used in the proposed algorithm is to initialize these ranges with unbounded values and to continuously tighten these ranges using localized timing constraints until an optimal solution is obtained. A branch and bound method is offered for the general retiming problem where the REC values are arbitrary. Certain monotonicity constraints can be placed on the REC values to permit the use of standard linear programming methods, thereby requiring significantly less computational time. These conditions and the feasibility of their application to practical circuits are presented. The algorithm is demonstrated on modified benchmark circuits and both increased clock frequencies and the elimination of all race conditions are observed.

Clock skew and delay characteristics associated with practical registers are significant factors affecting the retiming of synchronous circuits. Although work recently reported using branch and bound techniques offers a means for effective retiming taking these factors into account, the computational complexity involved is substantially greater than that associated with less general retiming algorithms that use standard linear programming methods. This paper presents sufficient conditions among values of localized clock skew and register characteristics which permit the retiming process to be achieved with a considerable reduction in computational complexity. The application of these conditions to some practical synchronous circuits is illustrated.