An adaptive physiology-aware communication framework for distributed medical cyber physical systems

For emergency medical cyber-physical systems, enhancing the safety and effectiveness of patient care, especially in remote rural areas, is essential. While the doctor to patient ratio in the United States is 30 to 10,000 in large metropolitan areas, it is only 5 to 10,000 in most rural areas; and the highest death rates are often found in the most rural counties. Use of telecommunication technologies can enhance effectiveness and safety of emergency ambulance transport of patients from rural areas to a regional center hospital. It enables remote monitoring of patients by the physician experts at the tertiary center. There are critical times during transport when physician experts can provide vital assistance to the ambulance Emergency Medical Technicians (EMT) to associate best treatments. However, the communication along the roads in rural areas can range irregularly from 4G to low speed 2G links, including some parts of routes with cellular network communication breakage. This unreliable and limited communication bandwidth together with the produced mass of clinical data and the many information exchanges pose a major challenge in real-time supervision of patients. In this study, we define the notion of distributed emergency care, and propose a novel adaptive physiology-aware communication framework which is aware of the patient condition, the underlying network bandwidth, and the criticality of clinical data in the context of the specific diseases. Using the concept of distributed medical CPS models, we study the semantics relation of communication Quality of Service (QoS) with clinical messages, criticality of clinical data, and an ambulance's undertaken route all in a disease-aware manner. Our proposed communication framework is aimed to enhance remote monitoring of acute patients during ambulance transport from a rural hospital to a regional center hospital. We evaluate the components of our framework through various experimentation phases including simulation, instrumentation, real-world profiling, and validation

A Fog Computing Architecture for Disaster Response Networks

In the aftermath of a disaster, the impacted communication infrastructure is unable to provide first responders with a reliable medium of communication. Delay tolerant networks that leverage mobility in the area have been proposed as a scalable solution that can be deployed quickly. Such disaster response networks (DRNs) typically have limited capacity due to frequent disconnections in the network, and under-perform when saturated with data. On the other hand, there is a large amount of data being produced and consumed due to the recent popularity of smartphones and the cloud computing paradigm. Fog Computing brings the cloud computing paradigm to the complex environments that DRNs operate in. The proposed architecture addresses the key challenges of ensuring high situational awareness and energy efficiency when such DRNs are saturated with large amounts of data. Situational awareness is increased by providing data reliably, and at a high temporal and spatial resolution. A waypoint placement algorithm places hardware in the disaster struck area such that the aggregate good-put is maximized. The Raven routing framework allows for risk-averse data delivery by allowing the user to control the variance of the packet delivery delay. The Pareto frontier between performance and energy consumption is discovered, and the DRN is made to operate at these Pareto optimal points. The FuzLoc distributed protocol enables mobile self-localization in indoor environments. The architecture has been evaluated in realistic scenarios involving deployments of multiple vehicles and devices

Software defined networking based resource management and quality of service support in wireless sensor network applications

To achieve greater performance in computing networks, a setup of critical computing aspects that ensures efficient network operation, needs to be implemented. One of these computing aspects is, Quality of Service (QoS). Its main functionality is to manage traffic queues by means of prioritizing sensitive network traffic. QoS capable networking allows efficient control of traffic especially for network critical data. However, to achieve this in Wireless Sensor Networks (WSN) is a serious challenge, since these technologies have a lot of computing limitations. It is even difficult to manage networking resources with ease in these types of technologies, due to their communication, processing and memory limitations. Even though this is the case with WSNs, they have been largely used in monitoring/detection systems, and by this proving their application importance. Realizing efficient network control requires intelligent methods of network management, especially for sensitive network data. Different network types implement diverse methods to control and administer network traffic as well as effectively manage network resources. As with WSNs, communication traffic and network resource control are mostly performed depending on independently employed mechanisms to deal with networking events occurring on different levels. It is therefore challenging to realize efficient network performance with guaranteed QoS in WSNs, given their computing limitations. Software defined networking (SDN) is advocated as a potential paradigm to improve and evolve WSNs in terms of capacity and application. A means to apply SDN strategies to these compute-limited WSNs, formulates software defined wireless sensor networks (SDWSN). In this work, a resource-aware OpenFlow-based Active Network Management (OF-ANM) QoS scheme that uses SDN strategies is proposed and implemented to apply QoS requirements for managing traffic congestion in WSNs. This scheme uses SDN programmability strategies to apply network QoS requirements and perform traffic load balancing to ensure congestion control in SDWSN. Our experimental results show that the developed scheme is able to provide congestion avoidance within the network. It also allows opportunities to implement flexible QoS requirements based on the system’s traffic state. Moreover, a QoS Path Selection and Resource-associating (Q-PSR) scheme for adaptive load balancing and intelligent resource control for optimal network performance is proposed and implemented. Our experimental results indicate better performance in terms of computation with load balancing and efficient resource alignment for different networking tasks when compared with other competing schemes.Thesis (PhD)--University of Pretoria, 2018.National Research FoundationUniversity of PretoriaElectrical, Electronic and Computer EngineeringPhDUnrestricte

H.264-based Low Power Heterogeneous Video Recording System

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 김수환.최근 영상 저장 장치의 사용이 증가함에 따라 한정된 배터리에서 저전력으로 영상 저장 장치를 동작하는 것이 중요하다. 영상 저장 장치에서 장기 저장을 수행하기 위해서는 영상 압축이 필수적이다. 기존 영상 저장 장치에서 영상 압축을 위해 보편적으로 사용되는 H.264/AVC 영상 압축 표준은 높은 압축률을 자랑하지만 높은 복잡도와 프레임 간의 인터 프레임 예측의 사용으로 전력 소모가 크다는 문제점을 갖는다. 본 논문에서는 이러한 문제점을 해결하기 위하여 두 가지 접근 기법을 통해 영상 저장 장치의 소모 전력 중, 가장 큰 비중을 차지하는 영상 압축에 소모 되는 전력을 감소시킨다. 우선, 멀티 압축 모듈을 통한 영상 저장 장치를 활용한다. Discrete Wavelet Transform과 Set Partitioning in Hierarchical Trees 압축에 기반한 경량화 압축 방식은 상대적으로 간단한 압축 방식으로 압축 효율은 H.264/AVC 인코더에 비해 낮으나 훨씬 더 적은 전력 소모로 동작할 수 있다. 본 논문에서는 기존의 영상 저장 장치와 다르게 H.264/AVC 인코더뿐만 아니라 경량화 압축 방식을 영상 저장 장치에 함께 활용하여 저전력 영상 저장 장치를 구성한다. 모든 영상 정보가 장기 저장 되어 보관될 필요가 있는 것이 아니기 때문에 H.264/AVC 인코더보다 압축 효율은 다소 낮지만 훨씬 낮은 전력에서 동작할 수 있는 경량화 압축 방식을 임시 저장 용도로 사용하고 이 영상 정보가 장기 저장될 필요가 있을 경우에만 영상 압축을 위해 H.264/AVC 인코더를 사용한다. 본 논문에서는 경량화 압축 방식의 활용뿐만 아니라 다운 샘플링 기법을 영상 저장 장치에 활용하여 낮은 bitrate 영역에서 더욱 큰 전력 감소 효과를 얻는다. 이러한 멀티 압축 모듈을 통한 방식은 장기 저장의 비율이 높아지면 결국 H.264/AVC 인코더가 사용되는 비율이 높아져서 전력 감소 효과가 크지 않다. 이러한 약점을 보완하기 위해서는 H.264/AVC 인코더 자체의 소모 전력을 감소시키는 것이 중요하다. 그렇기 때문에 본 논문에서는 H.264/AVC 인코더 내부의 전력 소모를 제어하는 power-aware design 기법을 영상 저장 장치에 활용한다. Power-aware design은 최소의 성능 저하로 최대의 전력 감소 효과를 얻는 기법으로 다양한 저전력 알고리즘의 동작 옵션들의 조합들 중에서 최적화된 알고리즘의 조합들로 power-level table을 정의하고 이를 인코더에 적용한다. 최적화된 조합을 찾기 위하여 알고리즘들 간의 상관 관계를 고려하여 개별 알고리즘의 전력 감소 효과를 통해 전체 시스템의 전력 감소 효과를 예측하는 모델을 활용하는데 이러한 전력 예측 모델을 사용하면 최적화된 알고리즘들의 조합을 찾기 위한 시뮬레이션 횟수가 현저하게 감소되기 때문에 여러 저전력 알고리즘이 함께 사용되더라도 최적의 조합을 쉽게 구성할 수 있다. 본 논문에서는 더 뛰어난 성능을 얻기 위해서 입력 영상의 크기와 움직임 여부에 따라서 네 가지의 다른 power-level table을 제시하며 이러한 power-level table이 사전에 정의되기 때문에 최적화된 저전력 알고리즘의 조합들이 실시간으로 인코더에 적용될 수 있다. 본 논문에서는 영상 저장 장치의 전력 감소를 위해 제시된 멀티 압축 모듈을 활용하는 방식과 H.264/AVC 인코더 내부의 전력 소모를 감소하는 방식을 모두 지원하는 통합 영상 저장 장치를 구현하고 통합된 영상 저장 장치 상에서 장기 저장의 비율과 bitrate 목표에 따른 분석을 통하여 동작 상황에 가장 알맞은 최적화된 영상 저장 장치를 활용한다. 이러한 최적화된 영상 저장 장치는 최소한의 성능 저하로 기존의 영상 저장 장치 대비 최대 72.5%의 전력 감소 효과를 갖는다.초 록 i 목 차 iv 그림 목차 viii 표 목 차 xi 제 1 장 서 론 1 1.1 연구 배경 1 1.2 연구 내용 3 1.3 논문 구성 7 제 2 장 관련 연구 8 2.1 영상 저장 장치의 동작 8 2.2 H.264/AVC 영상 압축 표준 12 2.2.1 H.264/AVC 인코더의 동작 12 2.2.2 저전력 H.264/AVC 인코더를 위한 기존 연구 15 2.3 경량화 압축 방식 알고리즘 19 2.3.1 1차원 Discrete Wave Transform 19 2.3.2 Set Partitioning in Hierarchical Trees 20 2.3.3 1차원 경량화 압축 방식 기법 21 제 3 장 멀티 압축 모듈을 통한 영상 저장 장치 22 3.1 경량화 압축 방식의 구현 22 3.1.1 구현 방식 22 3.1.2 구현 결과 및 성능 비교 28 3.2 경량화 압축 방식을 통한 영상 저장 장치의 구현 34 3.2.1 LWC 기반 영상 저장 장치 35 3.2.2 D-LPFC 기반 영상 저장 장치 38 3.2.3 제안된 영상 저장 장치의 분석 42 3.3 성능 평가 45 3.3.1 전력 측정 방법 45 3.3.2 모드 별 전력 분석 48 3.3.3 FRECORD에 따른 시스템 전체 전력 및 성능 분석 52 제 4 장 H.264/AVC 자체적인 전력 감소 기법 55 4.1 H.264/AVC 자체 전력 감소의 필요성 55 4.2 Power-Aware Design 56 4.2.1 Power Level Table의 생성 56 4.2.2 입력 영상의 특징이 미치는 영향 59 4.2.3 전력 레벨의 유동적 선택 기법 60 4.2.4 전력 레벨 적용 알고리즘 62 4.3 전력 예측 모델 65 4.4 Power-Aware Design의 예시 71 4.4.1 네 가지 저전력 알고리즘 71 4.4.2 전력 예측 모델의 예시 73 4.4.3 개별 알고리즘의 전력 소모 측정 76 4.4.4 최적화된 알고리즘 옵션의 선택 84 4.4.5 Power level table의 생성의 예시 87 4.5 성능 평가 92 4.5.1 Power-Aware Design의 성능 측정 92 4.5.2 기존 Power-Aware Design과의 성능 비교 105 4.5.3 Power-Aware Design의 영상 저장 장치 적용 113 제 5 장 최적화된 영상 저장 장치의 활용 기법 115 5.1 통합 영상 저장 장치 116 5.1.1 통합 영상 저장 장치의 구현 116 5.1.2 통합 영상 저장 장치의 FPGA 검증 119 5.2 최적화된 영상 저장 장치 122 5.2.1 최적화된 영상 저장 장치를 위한 분석 122 5.2.2 최적화된 영상 저장 장치 선택 기법 130 5.3 성능 평가 132 제 6 장 결론 137 참고문헌 139 Abstract 144Docto

A cross-layer approach for optimizing the efficiency of wireless sensor and actor networks

Recent development has lead to the emergence of distributed Wireless Sensor and Actor Networks (WSAN), which are capable of observing the physical environment, processing the data, making decisions based on the observations and performing appropriate actions. WSANs represent an important extension of Wireless Sensor Networks (WSNs) and may comprise a large number of sensor nodes and a smaller number of actor nodes. The sensor nodes are low-cost, low energy, battery powered devices with restricted sensing, computational and wireless communication capabilities. Actor nodes are resource richer with superior processing capabilities, higher transmission powers and a longer battery life. A basic operational scenario of a typical WSAN application follows the following sequence of events. The physical environment is periodically sensed and evaluated by the sensor nodes. The sensed data is then routed towards an actor node. Upon receiving sensed data, an actor node performs an action upon the physical environment if necessary, i.e. if the occurrence of a disturbance or critical event has been detected. The specific characteristics of sensor and actor nodes combined with some stringent application constraints impose unique requirements for WSANs. The fundamental challenges for WSANs are to achieve low latency, high energy efficiency and high reliability. The latency and energy efficiency requirements are in a trade-off relationship. The communication and coordination inside WSANs is managed via a Communication Protocol Stack (CPS) situated on every node. The requirements of low latency and energy efficiency have to be addressed at every layer of the CPS to ensure overall feasibility of the WSAN. Therefore, careful design of protocol layers in the CPS is crucial in attempting to meet the unique requirements and handle the abovementioned trade-off relationship in WSANs. The traditional CPS, comprising the application, network, medium access control and physical layer, is a layered protocol stack with every layer, a predefined functional entity. However, it has been found that for similar types of networks with similar stringent network requirements, the strictly layered protocol stack approach performs at a sub-optimal level with regards to network efficiency. A modern cross-layer paradigm, which proposes the employment of interactions between layers in the CPS, has recently attracted a lot of attention. The cross-layer approach promotes network efficiency optimization and promises considerable performance gains. It is found that in literature, the adoption of this cross-layer paradigm has not yet been considered for WSANs. In this dissertation, a complete cross-layer enabled WSAN CPS is developed that features the adoption of the cross-layer paradigm towards promoting optimization of the network efficiency. The newly proposed cross-layer enabled CPS entails protocols that incorporate information from other layers into their local decisions. Every protocol layer provides information identified as beneficial to another layer(s) in the CPS via a newly proposed Simple Cross-Layer Framework (SCLF) for WSANs. The proposed complete cross-layer enabled WSAN CPS comprises a Cross-Layer enabled Network-Centric Actuation Control with Data Prioritization (CL-NCAC-DP) application layer (APPL) protocol, a Cross-Layer enabled Cluster-based Hierarchical Energy/Latency-Aware Geographic Routing (CL-CHELAGR) network layer (NETL) protocol and a Cross-Layer enabled Carrier Sense Multiple Access with Minimum Preamble Sampling and Duty Cycle Doubling (CL-CSMA-MPS-DCD) medium access control layer (MACL) protocol. Each of these protocols builds on an existing simple layered protocol that was chosen as a basis for development of the cross-layer enabled protocols. It was found that existing protocols focus primarily on energy efficiency to ensure maximum network lifetime. However, most WSAN applications require latency minimization to be considered with the same importance. The cross-layer paradigm provides means of facilitating the optimization of both latency and energy efficiency. Specifically, a solution to the latency versus energy trade-off is given in this dissertation. The data generated by sensor nodes is prioritised by the APPL and depending on the delay-sensitivity, handled in a specialised manor by every layer of the CPS. Delay-sensitive data packets are handled in order to achieve minimum latency. On the other hand, delay-insensitive non critical data packets are handled in such a way as to achieve the highest energy efficiency. In effect, either latency minimization or energy efficiency receives an elevated precedence according to the type of data that is to be handled. Specifically, the cross-layer enabled APPL protocol provides information pertaining to the delay-sensitivity of sensed data packets to the other layers. Consequently, when a data packet is detected as highly delay-sensitive, the cross-layer enabled NETL protocol changes its approach from energy efficient routing along the maximum residual energy path to routing along the fastest path towards the cluster-head actor node for latency minimizing of the specific packet. This is done by considering information (contained in the SCLF neighbourhood table) from the MACL that entails wakeup schedules and channel utilization at neighbour nodes. Among the added criteria, the next-hop node is primarily chosen based on the shortest time to wakeup. The cross-layer enabled MACL in turn employs a priority queue and a temporary duty cycle doubling feature to enable rapid relaying of delay-sensitive data. Duty cycle doubling is employed whenever a sensor node’s APPL state indicates that it is part of a critical event reporting route. When the APPL protocol state (found in the SCLF information pool) indicates that the node is not part of the critical event reporting route anymore, the MACL reverts back to promoting energy efficiency by disengaging duty cycle doubling and re-employing a combination of a very low duty cycle and preamble sampling. The APPL protocol conversely considers the current queue size of the MACL and temporarily halts the creation of data packets (only if the sensed value is non critical) to prevent a queue overflow and ease congestion at the MACL By simulation it was shown that the cross-layer enabled WSAN CPS consistently outperforms the layered CPS for various network conditions. The average end-to-end latency of delay-sensitive critical data packets is decreased substantially. Furthermore, the average end-to-end latency of delay-insensitive data packets is also decreased. Finally, the energy efficiency performance is decreased by a tolerable insignificant minor margin as expected. The trivial increase in energy consumption is overshadowed by the high margin of increase in latency performance for delay-sensitive critical data packets. The newly proposed cross-layer CPS achieves an immense latency performance increase for WSANs, while maintaining excellent energy efficiency. It has hence been shown that the adoption of the cross-layer paradigm by the WSAN CPS proves hugely beneficial with regards to the network efficiency performance. This increases the feasibility of WSANs and promotes its application in more areas.Dissertation (MEng)--University of Pretoria, 2009.Electrical, Electronic and Computer Engineeringunrestricte

Dynamic Resource Management of Network-on-Chip Platforms for Multi-stream Video Processing

This thesis considers resource management in the context of parallel multiple video stream decoding, on multicore/many-core platforms. Such platforms have tens or hundreds of on-chip processing elements which are connected via a Network-on-Chip (NoC). Inefficient task allocation configurations can negatively affect the communication cost and resource contention in the platform, leading to predictability and performance issues. Efficient resource management for large-scale complex workloads is considered a challenging research problem; especially when applications such as video streaming and decoding have dynamic and unpredictable workload characteristics. For these type of applications, runtime heuristic-based task mapping techniques are required. As the application and platform size increase, decentralised resource management techniques are more desirable to overcome the reliability and performance bottlenecks in centralised management. In this work, several heuristic-based runtime resource management techniques, targeting real-time video decoding workloads are proposed. Firstly, two admission control approaches are proposed; one fully deterministic and highly predictable; the other is heuristic-based, which balances predictability and performance. Secondly, a pair of runtime task mapping schemes are presented, which make use of limited known application properties, communication cost and blocking-aware heuristics. Combined with the proposed deterministic admission controller, these techniques can provide strict timing guarantees for hard real-time streams whilst improving resource usage. The third contribution in this thesis is a distributed, bio-inspired, low-overhead, task re-allocation technique, which is used to further improve the timeliness and workload distribution of admitted soft real-time streams. Finally, this thesis explores parallelisation and resource management issues, surrounding soft real-time video streams that have been encoded using complex encoding tools and modern codecs such as High Efficiency Video Coding (HEVC). Properties of real streams and decoding trace data are analysed, to statistically model and generate synthetic HEVC video decoding workloads. These workloads are shown to have complex and varying task dependency structures and resource requirements. To address these challenges, two novel runtime task clustering and mapping techniques for Tile-parallel HEVC decoding are proposed. These strategies consider the workload communication to computation ratio and stream-specific characteristics to balance predictability improvement and communication energy reduction. Lastly, several task to memory controller port assignment schemes are explored to alleviate performance bottlenecks, resulting from memory traffic contention

D6.6 Final report on the METIS 5G system concept and technology roadmap

This deliverable presents the METIS 5G system concept which was developed to fulfil the requirements of the beyond-2020 connected information society and to extend today’s wireless communication systems to include new usage scenarios. The METIS 5G system concept consists of three generic 5G services and four main enablers. The three generic 5G services are Extreme Mobile BroadBand (xMBB), Massive Machine- Type Communications (mMTC), and Ultra-reliable Machine-Type Communication (uMTC). The four main enablers are Lean System Control Plane (LSCP), Dynamic RAN, Localized Contents and Traffic Flows, and Spectrum Toolbox. An overview of the METIS 5G architecture is given, as well as spectrum requirements and considerations. System-level evaluation of the METIS 5G system concept has been conducted, and we conclude that the METIS technical objectives are met. A technology roadmap outlining further 5G development, including a timeline and recommended future work is given.Popovski, P.; Mange, G.; Gozalvez -Serrano, D.; Rosowski, T.; Zimmermann, G.; Agyapong, P.; Fallgren, M.... (2014). D6.6 Final report on the METIS 5G system concept and technology roadmap. http://hdl.handle.net/10251/7676

State-of-the-Art Sensors Technology in Spain 2015: Volume 1

This book provides a comprehensive overview of state-of-the-art sensors technology in specific leading areas. Industrial researchers, engineers and professionals can find information on the most advanced technologies and developments, together with data processing. Further research covers specific devices and technologies that capture and distribute data to be processed by applying dedicated techniques or procedures, which is where sensors play the most important role. The book provides insights and solutions for different problems covering a broad spectrum of possibilities, thanks to a set of applications and solutions based on sensory technologies. Topics include: • Signal analysis for spectral power • 3D precise measurements • Electromagnetic propagation • Drugs detection • e-health environments based on social sensor networks • Robots in wireless environments, navigation, teleoperation, object grasping, demining • Wireless sensor networks • Industrial IoT • Insights in smart cities • Voice recognition • FPGA interfaces • Flight mill device for measurements on insects • Optical systems: UV, LEDs, lasers, fiber optics • Machine vision • Power dissipation • Liquid level in fuel tanks • Parabolic solar tracker • Force sensors • Control for a twin roto

The Next Generation Intelligent Transportation System: Connected, Safe and Green

Modern Intelligent Transportation Systems (ITSs) employ communication technologies in order to ameliorate the passenger's commuting experience. Vehicular Networking lies at the core of inaugurating an efficient transportation system and aims at transforming vehicles into smart mobile entities that are able to sense their surroundings, collect information about the environment and communicate with each other as well as with Roadside Units (RSUs) deployed alongside roadways. As such, the novel communication paradigm of vehicular networking gave birth to an ITS that embraces a wide variety of applications including but not limited to: traffic management, passenger and road safety, environment monitoring and road surveillance, hot-spot guidance, Drive Thru Internet access, remote region connectivity, and so forth. Furthermore, with the rapid development of computation and communication technologies, the Internet of Vehicles (IoV) promises huge commercial interest and research value, thereby attracting a significant industrial and academic attention. This thesis studies and analyses fundamentally challenging problems in the context of vehicular environments and proposes new techniques targeting the improvement of the performance of ITSs envisioned to play a remarkable role in the IoV era. Unlike existing wireless mobile networks, vehicular networks possess unique characteristics, including high node mobility and a rapidly-changing topology, which should be carefully accounted for. Four major problems from the pool of existing vehicular networking persisting challenges will be addressed in this thesis, namely: a) establishing a connectivity path in a highly dynamic Vehicular Ad Hoc Network, b) examining the performance of Vehicle-to-Infrastructure communication Medium Access Control schemes, c) addressing the scheduling problem of a vehicular networking scenario encompassing an energy-limited RSU by exploiting machine learning techniques, particularly reinforcement learning, to train an agent to make appropriate decisions and develop a scheduling policy that prolongs the network's operational status and allows for acceptable Quality-of-Service levels and d) overcoming the limitations of reinforcement learning techniques in high-dimensional input scenarios by exploiting recent advances in deep learning in an effort to satisfy the driver's well-being as well as his demand for continuous connectivity in a green, balanced, connected and efficient vehicular network. These problems will be extensively studied throughout this thesis, followed by discussions that highlight open research directions worth further investigations