Distributed in-network processing and resource optimization over mobile-health systems

Alaa Awad, Amr Mohamed, Carla-Fabiana Chiasserini, Tarek Elfouly
2017 Journal of Network and Computer Applications  
Advances in wireless and mobile communication technologies has promoted the development of Mobile-health (m-health) systems to find new ways to acquire, process, transport, and secure the medical data. M-health systems provide the scalability needed to cope with the increasing number of elderly and chronic disease patients requiring constant monitoring. However, the design and operation of such systems with Body Area Sensor Networks (BASNs) is challenging in twofold. First, limited energy,
more » ... tational and storage resources of the sensor nodes. Second, the need to guarantee application level Quality of Service (QoS). In this paper, we integrate wireless network components, and application-layer characteristics to provide sustainable, energy-efficient and high-quality services for m-health systems. In particular, we propose an Energy-Cost-Distortion (E-C-D) solution, which exploits the benefits of in-network processing and medical data adaptation to optimize the transmission energy consumption and the cost of using network services. Moreover, we present a distributed cross-layer solution, which is suitable for heterogeneous wireless m-health systems with variable network size. Our scheme leverages Lagrangian duality theory to find efficient trade-off among energy consumption, network cost, and vital signs distortion, for delay sensitive transmission of medical data. Simulation results show that the proposed scheme achieves the optimal trade-off between energy efficiency and QoS requirements, while providing 15% savings in the objective function (i.e., E-C-D utility function), compared to solutions based on equal bandwidth allocation. Providing decent healthcare services for the chronically ill and elderly people becomes a top national interest worldwide. The rising number of chronic disease patients, emergency and disaster management, which require continuous monitoring of human vital signs, have increased the importance of remote monitoring and mobile-health (m-health) systems. Such systems emerge as a promising approach to improve healthcare efficiency, where miniaturized wearable and implantable body sensor nodes and smartphones are utilized to provide remote healthcare monitoring in many situations like disaster management and early detection of diseases [1], [2] . In our work, we focus on the Electroencephalography (EEG)-based applications. The EEG signal is considered as the main source of information to study human brain, which plays an important role in diagnosis of epileptic disease, brain death, tumors, stroke and several brain disorders [3] . EEG signals also play a fundamental role in Brain Computer Interface (BCI) applications [4] . In our model, the Personal/Patient Data Aggregator (PDA), potentially represented by a smartphone, gathers sensed data from a group of sensor nodes, and then forwards the aggregate traffic to the M-Health Cloud (MHC). In this scenario, the patients equipped with smartphones and body area sensor networks (BASN) can walk freely while receiving high-quality healthcare monitoring from medical professionals anytime and anywhere. Although m-health systems have prominent benefits, they also exhibit peculiar design and operational challenges that need to be addressed. Among these are energy consumption, network performance, and quality of service (QoS) guarantee for the delivery of medical data. For example, in normal conditions, the medical patient's data is reported to the MHC every 5 minutes [5] . However, in case of emergency, the BASN starts reading a variety of medical measurements, hence, a large amount of data will be generated in a very short period of time. Furthermore, the sensed data should be reported every 10 seconds for high-intensive monitoring [6] . Thus, it is clear that in these cases, the smartphone energy consumption and the management of the overall network in a distributed fashion becomes of prominent importance. Additionally, scalability and robustness against changes in topology (i.e., adding new nodes or node failure) are important design issues in m-health systems. All these factors make centralized approaches not appropriate for being used in real world situations, especially over large networks, and point to the need of simple, efficient, and distributed algorithms. In addition to that, recording, processing, and transmitting large volumes of such data is challenging and may deem some of these applications impractical, especially for the increasing number of chronic disease patients that require continuous monitoring in highly populated cities. This has led to the emergence of smart health (s-health) concept, which is the context-aware evolution of m-health, leveraging mobile technologies to provide smart personalized health [7] . This rising evolution of intelligent systems, mobile communications, and s-health services has motivated us to leverage context-aware in-network processing at the PDA on the raw EEG data prior to transmission, while considering application characteristics, wireless transmission dynamics, and physical layer resources. Accordingly, in this paper, we propose a solution that enables energy-efficient high-quality patient health monitoring to facilitate remote chronic disease management. We propose a multiobjective optimization problem that targets different QoS metrics at the application layer like signal distortion, and at physical layer like transmission delay and Bit Error Rate (BER), as well as monetary cost and transmission energy. In particular, we aim to achieve the optimal trade-off among the above factors, which exhibit conflicting trends. The main contributions of
doi:10.1016/j.jnca.2017.01.014 fatcat:tsca4cqsxjhmpidzyecpnvcwme