Szymon Fedor
MIT Media Lab
Research Scientist
Digital Health, Machine Learning, Biomedical Engineering
Publications
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Wearable Technology to support clinical practice for Depressive Disorder
Real-time digital monitoring of a suicide attempt by a hospital patient
Suicide is among the most devastating problems facing clinicians, who currently have limited tools to predict and
prevent suicidal behavior. Here we report on real-time, continuous smartphone and sensor data collected before,
during, and after a suicide attempt made by a patient during a psychiatric inpatient hospitalization. We observed
elevated and persistent sympathetic nervous system arousal and suicidal thinking leading up to the suicide
attempt. This case provides the highest resolution data to date on the psychological, psychophysiological, and
behavioral markers of imminent suicidal behavior and highlights new directions for prediction and prevention
efforts.
The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.
Rising early warning signals in affect associated with future changes in depression: a dynamical systems approach
Predicting future states of psychopathology such as depressive episodes has been a hallmark initiative in mental health research. Dynamical systems theory has proposed that rises in certain ‘early warning signals’ (EWSs) in time-series data (e.g. auto-correlation, temporal variance, network connectivity) may precede impending changes in disorder severity. The current study investigates whether rises in these EWSs over time are associated with future changes in disorder severity among a group of patients with major depressive disorder (MDD).
Mixed Effects Random Forests for Personalised Predictions of Clinical Depression Severity
This work demonstrates how mixed effects random forests enable accurate predictions of depression severity using multimodal physiological and digital activity data collected from an 8-week study involving 31 patients with major depressive disorder. We show that mixed effects random forests outperform standard random forests and personal average baselines when predicting clinical Hamilton Depression Rating Scale scores (HDRS17). Compared to the latter baseline, accuracy is significantly improved for each patient by an average of 0.199-0.276 in terms of mean absolute error (p 0.05). This is noteworthy as these simple baselines frequently outperform machine learning methods in mental health prediction tasks. We suggest that this improved performance results from the ability of the mixed effects random forest to personalise model parameters to individuals in the dataset. However, we f ind that these improvements pertain exclusively to scenarios where labelled patient data are available to the model at training time. Investigating methods that improve accuracy when generalising to new patients is left as important future work.
Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors
While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be leveraged for measuring depression symptom severity. This study evaluates the feasibility and performance of assessing depressive symptom severity by using behavioral and physiological features obtained from wristband and smartphone sensors.
This exploratory study examined the effects of varying g-forces, including feelings of weightlessness, on an individual’s physiology during parabolic flight. Specifically, we collected heart rate, accelerometer, and skin conductance measurements from 16 flyers aboard a parabolic flight using wearable, wireless sensors. The biosignals were then correlated to participant reports of nausea, anxiety, and excitement during periods of altered g-forces. Using linear mixed-effects models, we found that (1) heart rate was positively correlated to individuals’ self-reported highest/lowest periods of both anxiety and excitement, and (2) bilateral skin conductance asymmetry was positively correlated to individuals’ self-reported highest/lowest periods of nausea.
Negative affect is more strongly associated with suicidal thinking among suicidal patients with borderline personality disorder than those without
Patients suffering from borderline personality disorder (BPD) are at elevated risk for suicidal thoughts and behaviors (STBs), but this well-described and clinically important association is not well-understood. Prior research suggests that STBs often function as an attempt to escape aversive affect, and that people with BPD experience stronger emotion reactivity and greater discomfort with emotion than those without BPD. Here, we tested whether negative affective states are more likely to predict suicidal thoughts among those with BPD than those without this disorder. Data on affective states and suicidal thoughts were collected several times per day from 35 psychiatric inpatients using their smartphones to capture real-time associations between negative affect and suicidal thoughts. Results revealed that the association between negative affective states (e.g., abandonment, desperation, guilt, hopelessness, loneliness, rage, self-hatred, and upset), and severity of suicidal thinking was stronger among those with BPD than among those without BPD. This finding has implications for risk assessment and intervention in the clinical setting: for a given degree of reported negative affect, patients with BPD experience more suicidal ideation than those without. Further research needs to be done to elucidate the mechanism of this effect.
To examine whether there are subtypes of suicidal thinking using real-time digital monitoring, which allows for the measurement of such thoughts with greater temporal granularity than ever before possible.
Objective assessment of depressive symptoms with machine learning and wearable sensors data
Abstract—Depression is the major cause of years lived in disability world-wide; however, its diagnosis and tracking methods still rely mainly on assessing self-reported depressive symptoms, methods that originated more than fifty years ago. These methods, which usually involve filling out surveys or engaging in face-to-face interviews, provide limited accuracy and reliability and are costly to track and scale. In this paper, we develop and test the efficacy of machine learning techniques applied to objective data captured passively and continuously from E4 wearable wristbands and from sensors in an Android phone for predicting the Hamilton Depression Rating Scale (HDRS). Input data include electrodermal activity (EDA), sleep behavior, motion, phone-based communication, location changes, and phone usage patterns. We introduce our feature generation and transformation process, imputing missing clinical scores from self-reported measures, and predicting depression severity from continuous sensor measurements. While HDRS ranges between 0 and 52, we were able to impute it with 2.8 RMSE and predict it with 4.5 RMSE which are low relative errors. Analyzing the features and their relation to depressive symptoms, we found that poor mental health was accompanied by more irregular sleep, less motion, fewer incoming messages, less variability in location patterns, and higher asymmetry of EDA between the right and the left wrists.
Location Patterns from Phone Sensors May Help Predict Depressive Symptoms: A Longitudinal Pilot Study
Examination of real-time fluctuations in suicidal ideation and its risk factors: Results from two ecological momentary assessment studies.
Abstract: Two studies examined 2 important but previously unanswered questions about the experience of suicidal ideation: (a) How does suicidal ideation vary over short periods of time?, and (b) To what degree do risk factors for suicidal ideation vary over short periods and are such changes associated with changes in suicidal ideation? Participants in Study 1 were 54 adults who had attempted suicide in the previous year and completed 28 days of ecological momentary assessment (EMA; average of 2.51 assessments per day; 2,891 unique assessments). Participants in Study 2 were 36 adult psychiatric inpatients admitted for suicide risk who completed EMA throughout their time in the hospital (average stay of 10.32 days; average 2.48 assessments per day; 649 unique assessments). These studies revealed 2 key findings: (a) For nearly all participants, suicidal ideation varied dramatically over the course of most days: more than 1-quarter (Study 1 ! 29%; Study 2 ! 28%) of all ratings of suicidal ideation were a standard deviation above or below the previous response from a few hours earlier and nearly all (Study 1 ! 94.1%; Study 2 ! 100%) participants had at least 1 instance of intensity of suicidal ideation changing by a standard deviation or more from 1 response to the next. (b) Across both studies, well-known risk factors for suicidal ideation such as hopelessness, burdensomeness, and loneliness also varied considerably over just a few hours and correlated with suicidal ideation, but were limited in predicting short-term change in suicidal ideation. These studies represent the most fine-grained examination of suicidal ideation ever conducted. The results advance the understanding of how suicidal ideation changes over short periods and provide a novel method of improving the short-term prediction of suicidal ideation.
Response to Commentaries on "Multiple Arousal Theory and Daily-Life Electrodermal Activity Asymmetry"
We respond to the commentaries of Critchley and Nagai, Mendes, Norman, Sabatinelli, and Richter. We agree that a theory needs to make predictions and we elaborate on the predictions we made so far. We do not agree that arousal has to have a precise definition in order to present theory about it; however, we do provide concrete answers to questions raised about multiple arousal theory.
Multiple arousal theory and daily-life electrodermal activity asymmetry
Abstract: Using “big data” from sensors worn continuously outside the lab, researchers have observed patterns of objective physiology that challenge some of the long-standing theoretical concepts of emotion and its measurement. One challenge is that emotional arousal, when measured as sympathetic nervous system activation through electrodermal activity, can sometimes differ significantly across the two halves of the upper body. We show that traditional measures on only one side may lead to misjudgment of arousal. This article presents daily life and controlled study data, as well as existing evidence from neuroscience, supporting the influence of multiple emotional substrates in the brain causing innervation on different sides of the body. We describe how a theory of multiple arousals explains the asymmetric EDA findings.
Can We Predict Depression From the Asymmetry of Electrodermal Activity?
Background
Historically, diagnosing and tracking depressive symptoms has been accomplished by assessing subjective diagnostic criteria, either from the DSM, or from standardized rating scales. Though useful for semantic and billing purposes, this approach has limited utility for
- determining subtypes of depression;
- capturing variations over relatively short time periods (i.e., over the course of a day), and
- predicting the course of the illness.
Despite recent research efforts, no clinically useful, non-invasive, inexpensive biomarkers for the diagnosis and prognosis of depression have been identified.
Objective:
Therefore, there is a critical need to identify and discover objective biomarkers for the diagnosis, prognosis, and treatment of depression. Brain imaging and recent findings have led us to hypothesize that depression, especially of the anxious type, might lead to larger right amygdala activation than left in most right-handers, and that this would map to larger electrodermal activity (EDA) on the right than on the left.
A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network
Abstract: Contiki’s Cooja is a very popular Wireless Sensor Network (WSN) simulator, but it lacks support for modelling sensing coverage. We introduce WSN-Maintain, a Cooja-based tool for maintaining coverage requirements in an in-building WSN. To analyse the coverage of a building, WSN-Maintain takes as input the floorplan of the building, the coverage requirement of each region and the locations of sensor nodes. We take account of the heterogeneity of device speci cations in terms of communication capability and sensing coverage. WSN-Maintain is run in parallel with the collect-view tool of Contiki, which was integrated into the Cooja simulator. We show that WSN-Maintain is able to automatically turn on redundant nodes to maintain the coverage requirementwhen active nodes fail and report failures that require physical maintenance. This tool allows us to evaluate different approaches to maintain coverage, including deferring physical maintenance to reduce operational costs.
Wavelet-based motion artifact removal for electrodermal activity
Abstract: Electrodermal activity (EDA) recording is a powerful, widely used tool for monitoring psychological or physiological arousal. However, analysis of EDA is hampered by its sensitivity to motion artifacts. We propose a method for removing motion artifacts from EDA, measured as skin conductance (SC), using a stationary wavelet transform (SWT). We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level (SCL) and skin conductance responses (SCRs).
Separation of Ambulatory Skin Conductance in Day and Sleep Activities based on Activity Magnitude and Sleep-Wake Scoring
Abstract: Ambulatory skin conductance (SC) signals often need to be analyzed independently for different user activities. As an ambulatory SC sensor is usually combined with an accelerometer, we examined its measurements to identify if a user is sitting, walking and running. We present our method for estimating the activities and how SC signals are distributed across daytime and sleep contexts.
PyFUNS: A python framework for ubiquitous networked sensors
Abstract. In recent years Wireless Sensor Networks (WSNs) have been deployed in wide range of applications from the health and environment monitoring to building and industrial control. However, the pace of prevalence of WSN is slower than anticipated by the research community due to several reasons including required embedded systems expertise for developing and deploying WSNs; use of proprietary protocols; and limits in scalability and reliability. In this paper we propose PyFUNS (Pythonbased Framework for Ubiquitous Networked Sensors) to address these
challenges. PyFUNS handles low level and networking functionalities, using the services provided by Contiki, and leaves to the user only the task of application development in the form of Python scripts. This approach reduces required expertise in embedded systems to develop WSN based applications. PyFUNS also uses 6LoWPAN and CoAP standard protocols to enable interoperability and ease of integration with other systems, pursuing the Internet of Things vision. Through a real implementation
of PyFUNS in two constrained platforms we proved its feasibility in mote devices, as well as its performance in terms of control delay, energy consumption and network traffic in several network topologies. As it is possible with PyFUNS to easily compare performance of different deployments of distributed application, PyFUNS can be used to identify optimal design of distributed application.
Automatic identification of artifacts in electrodermal activity data
Abstract: Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a
physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.
Active learning for electrodermal activity classification
Abstract: To filter noise or detect features within physiological signals, it is often effective to encode expert knowledge into a model such as a machine learning classifier. However, training such a model can require much effort on the part of
the researcher; this often takes the form of manually labeling portions of signal needed to represent the concept being trained. Active learning is a technique for reducing human effort by developing a classifier that can intelligently select the most relevant data samples and ask for labels for only those samples, in an iterative process. In this paper we demonstrate that active learning can reduce the labeling effort required of researchers by as much as 84% for our application, while
offering equivalent or even slightly improved machine learning performance.
A visual programming framework for wireless sensor networks in smart home applications
In this paper, we build upon the Internet of Things (IoT) paradigm, with aim of delivering networked solutions that enable to connect not only single sensors, but also whole wireless sensor networks (WSN) to the Internet in a secure, simple and efficient way, and describe the design and implementation of a smart-home management
system. The system is composed of a lightweight tool with an intuitive user interface for commissioning of IPenabled WSN with constrained capabilities. The solution includes a visual programming interface with a common framework for discovering smart home services on the constrained WSN, and a code analysis and translation engine to generate python code. This engine analyses the application rules defined with the graphical user interface and translates them into distributed application scripts. The system also includes modules to plan the optimization of the deployment, and deploy and start the generated code. A prototype of the system, with the visual programming solution and code generation module developed is presented in this paper.
Verification of compatibility among telecommunication network features
Abstract: A method and apparatus for assessing the compatibility of a selected network feature with the network features of an existing telecommunications network comprises providing an abstraction of each network feature in the form of a data model which defines the relationships between any network elements or nodes, software elements and software features required for each network feature. Information is then collected about the existing telecommunications network to identity the deployment of the network features in the existing network. This information is then combined with the data model to build a feature compatibility matrix which defines the compatibility of network features in the existing telecommunications network. The matrix can then be interrogated with information defining the parameters of the selected feature to be added or upgraded to determine whether the selected feature is compatible with features of the existing network.
Service Discovery Protocols for Constrained Machine-to-Machine Communications
Abstract: An emerging trend in many applications is to use resource-constrained wireless devices for machine-to-machine (M2M) communications. The observed proliferation of wireless embedded systems is expected to have a significant impact on future M2M applications if the services provided can be automatically discovered and accessed at runtime. In order to realize the decoupling of M2M applications and services, energy efficient service discovery mechanisms must be designed so as to minimize human intervention during configuration and management phases. However, many traditional service discovery protocols cannot be applied to wireless constrained devices because they introduce too much overhead, fail in a duty-cycled environment or require significant memory resources. To address this, either new protocols are being proposed or existing ones are adapted to meet the requirements of constrained networks. In this article, we provide a
comprehensive overview of service discovery protocols that have been recently proposed for constrained M2M communications by the Internet Engineering Task Force (IETF). Advantages, disadvantages, performance and challenges of the existing solutions for different M2M scenarios are also analyzed.
Ambulatory EDA: Comparisons of bilateral forearm and calf locations
Abstract: The growing need for ambulatory measurement of sympathetic nervous system arousal makes it important to find an unobtrusive alternative to the palmar site for long-term measurement of electrodermal activity (EDA), where sensors may need to be worn for a month or longer. Two prior studies have shown that EDA measured on the palmar and forearm sites is highly correlated; consequently, in this work we examine EDA measured simultaneously from the left and right forearm and left and right calf locations on the bodies of healthy adult volunteers (n=32), sites that support long-term wear. Time-synchronized measurements are made while each participant experiences three types of stressors: physical, cognitive, and emotional, preceded and followed by four rest periods. We also examine the lag of EDA response times in the physical task. All multi-site cross-correlations for all tasks and rest periods had median correlation coefficients above 0.5. The bilateral EDA measurements between both calves have the highest correlation coefficients (mean = 0.91, median = 0.96) calculated over the entire experiment, followed by the correlation coefficients between the forearms (mean=0.78, median=0.91). Participants who reported regularly playing sport showed faster EDA responses to the physical task than those who were less active. All participants reported the four locations to be comfortable, while 40% of participants reported the calf to be slightly more comfortable than the forearm. This study suggests that the back of the lower calf is a viable site for long term measurement of EDA.
A Neighbour Disjoint Multipath Scheme for Fault Tolerant Wireless Sensor Networks
Abstract: In this paper, we propose a “Neighbour Disjoint Multipath (NDM)” scheme that increases resilience against node or link failures in a wireless sensor network (WSN). Our algorithm chooses the shortest path between a sensor and the
sink as the primary path, thus ensuring the algorithm is energy efficient under normal circumstances. In selecting the backup paths, we utilise the disjoint property to ensure that i) when there are k paths between source and sink, no set of k node failures can result in total communication break between them,
and ii) by having (k − 1) spatially separated backup paths w.r.t. the primary path, the probability of simultaneous failure of the primary and backup paths is reduced in case of localised poor channel quality or node failures. Our algorithm not only ensures the node disjointedness characteristics of the constructed paths, but also tries to minimise the impact of localised node or link failures where a localised portion of the network may be unusable. We analyse the motivation behind our idea clearly, and discuss the algorithm in detail. We also compare the NDM scheme with other common multipath techniques such as node-disjoint and edge-disjoint approaches, and point out its effectiveness through
simulation.
Self-organisation for building automation systems: Middleware linc as an integration tool
Abstract: Building Automation usually involves a large number of systems that should cooperate in
order to improve e.g. the user comfort, security, but also decrease the overall energy consumption. One aim of the EU funded SCUBA project is to improve the coordination among devices and systems installed in a building. This paper deals with the extension of a framework dedicated to Building Automation and built on top of the LINC resource-based middleware. Two particular tools developed in SCUBA and that make use of the middleware are also presented together with the encapsulation of the ontology-based Building Automation System model named BASOnt.
Architecture for self-organizing, co-operative and robust Building Automation Systems
Abstract: This paper provides an overview of the architecture for self-organizing, co-operative and robust Building Automation Systems (BAS) proposed by the EC funded FP7 SCUBA1 project. We describe the current situation in monitoring and control systems and outline the typical stakeholders involved in the case of building automation systems. We derive seven typical use cases which will be demonstrated and evaluated on
pilot sites. From these use cases the project designed an architecture relying on six main modules that realize the design, commissioning and operation of self-organizing, co-operative,robust BAS.
A Cooja-based tool for maintaining sensor network coverage requirements in a building
Abstract: Contiki’s Cooja is a very popular Wireless Sensor Network (WSN) simulator, but it lacks support for modelling sensing coverage. We introduce WSN-Maintain, a Cooja-based tool for maintaining coverage requirements in an in-building WSN. To analyse the coverage of a building, WSN-Maintain takes as input the floorplan of the building, the coverage requirement of each region and the locations of sensor nodes. We take account of the heterogeneity of device speci cations in terms of communication capability and sensing coverage. WSN-Maintain is run in parallel with the collect-view tool of Contiki, which was integrated into the Cooja simulator. We show that WSN-Maintain is able to automatically turn on redundant nodes to maintain the coverage requirement when active nodes fail and report failures that require physical maintenance. This tool allows us to evaluate different approaches to maintain coverage, including deferring physical maintenance to reduce operational costs.
Constrained application protocol for low power embedded networks: A survey
Abstract: IPv6 will make it possible to provide Internet connectivity to any device. In the same line, Web technologies will make managing, communicating and visualizing any information
provided by these devices attractive to the end users and application developers. Most of the new devices connected to this Web of Things (WoT) will be embedded and wirelessly connected.
However, current Web technologies, developed with powerful devices in mind, will not be suited for this kind of environment. In order to make the WoT a reality for low power embedded networks, specialised protocols that consider the energy, memory and processing constraints of these devices must be designed. The IETF recently created the CoRE group whose first goal has been developing a RESTful application layer protocol for communications within embedded wireless networks referred to
as Constrained Application Protocol (CoAP). The year 2011 has seen a big push with regards to research in this area, indicating a growing interest in the community towards RESTful interactions
in low power wireless embedded networks. This paper surveys current research efforts on the Constrained Application Protocol for low power embedded networks.
Commissioning of low power embedded devices with IPv6/CoAP
Abstract: The commissioning of sensors and actuators within a building is often carried out by an operator who manually gathers and later inserts configuration data into the building management system. Data sets collected can easily reach hundreds which makes this manual process a slow complex
operation which in turn is prone to errors. In this paper, we present a client-server architecture for a simple web based commissioning application, based on the Constrained Application Protocol (CoAP), that allows an operator to easily discover and browse through newly installed devices in order to perform commissioning configurations onsite.
Sensor Networks’ Integration
Sensor networks and applications thereof have been intensively researched in the past decade and a variety of systems have been meanwhile deployed in real-world settings. Most of these applications and the corresponding sensor networks they use are designed as vertically integrated systems. In such vertical systems, a sensor network or a limited set of mostly homogeneous sensor networks are deployed for a specific application in mind. The application is mostly the sole user of this sensor network and has a priori knowledge of the capabilities that the sensor network(s) provides. An application also typically knows how to address the respective gateways/ sinks of the sensor networks, in order to interact with the sensor networks and shares a common interaction protocol with them.
As the number of the sensor networks that may be used by an application grows, it is becoming increasingly cumbersome for applications to manage direct interactions between those. Furthermore, the reuse of the existing sensor network infrastructure for multiple applications could avoid redundant deployment of similar sensor networks at the same location and provide higher returns for the initial investments costs of the deployed sensor network infrastructure. Recent research has therefore focused on overcoming the inflexibility of the tightly coupled vertical system and proposed several sensor network integration frameworks. These frameworks aim to break up the vertical systems into horizontal reusable system components and make them available to a larger set of applications. The frameworks typically provide support functionality that significantly reduces the interaction complexity of applications and eases incremental deployment of new sensor networks. Via these frameworks, applications can gain access to a large variety of connected geographically distributed sensor networks.
While representing first stepping stones for a real-world Internet, a variety of different issues remain unaddressed whereas they are essential for realizing an ecosystem for real-world contexts and interactions. Therefore, the development of sensor network integration frameworks is currently being carried by many industrial and academic institutions. In this chapter, an overview of existing sensor network integration frameworks (SNIFs) is presented, highlighting the main concepts and key features. Various examples of these frameworks are provided covering different design approaches from both industrial and academic organizations. Each of these frameworks is briefly analyzed with the description of key features and innovative solutions. Also their potential limitations and shortcomings are highlighted.
Mobile application and wearable sensors for use in cognitive behavioral therapy for drug addiction and PTSD
Abstract: We present a mobile system for cognitive behavioral therapy (CBT) developed for an ongoing study for patients with drug-addiction and post-traumatic stress disorder (PTSD). The mobile platform consists of two parts: a wearable sensor system for collecting algorithm training data in the lab, and a mobile phone application used to
deliver therapeutic interventions as triggered by real-time
sensor data. Ecological momentary assessments (EMA) are also used as a means of collecting subjective data and validating the sensor classification algorithm. We provide a brief description of the wearable sensors, mobile phone software and network architecture used in the study.
Magneto approach to QoS monitoring
Abstract: Quality of Service (QoS) monitoring of end-user services is an integral and indispensable part of service management. However in large, heterogeneous and complex networks where there are many services, many types of end-user devices, and huge numbers of subscribers, it is not trivial to monitor QoS and estimate the status of Service Level Agreements (SLAs). Furthermore, the overwhelming majority of end-terminals do not provide precise information about QoS which aggravates the difficulty of keeping track of SLAs. In this paper, we describe a solution that combines a number of techniques in a novel and unique way to overcome the complexity and difficulty of QoS monitoring. Our solution uses a model driven approach to service modeling, data mining techniques on small sample sets of terminal QoS reports (from “smarter” end-user devices), and network level key performance indicators (N-KPIs) from probes to address this problem. Service modeling techniques empowered with a modeling engine and a purpose-built language hide the complexity of SLA status monitoring. The data mining technique uses its own engine and learnt data models to estimate QoS values based on N-KPIs, and feeds the estimated values to the modeling engine to calculate SLAs. We describe our solution, the prototype and experimental results in the paper.
Cross-layer routing and time synchronisation in wireless sensor networks
Abstract: A common time reference across nodes is required in most Wireless Sensor Networks (WSNs) applications. It is needed, for example, to time-stamp sensor samples and for long-term duty cycling of nodes. Also many routing protocols require that nodes communicate according to some predefined schedule for reasons of energy efficiency. However, independent distribution of the time information, without considering the routing algorithm schedule or network topology may lead to a failure of the synchronisation protocol. This was confirmed empirically, and was shown to result in loss of connectivity. This can be avoided by integrating the synchronisation service into the network layer with a so-called cross-layer approach. This approach introduces interactions between the layers of a conventional layered network stack, so that the routing layer may share information with other layers. We explore whether energy efficiency can be enhanced through the use of cross-layer optimisations and present two novel cross-layer routing algorithms. The first protocol, designed for hierarchical, cluster based networks and called CLEAR (Cross Layer Efficient Architecture for Routing), uses the routing algorithm to distribute time information which can be used for e±cient duty cycling of nodes. The second method – called RISS (Routing Integrated Synchronization Service) – integrates time synchronization into the network layer and is designed to work well in flat, non-hierarchical network topologies.
We implemented and tested the performance of these solutions in simulations and also deployed these routing techniques on sensor nodes using TinyOS. We compared the average power consumption of the nodes and the precision of time synchronization with the corresponding parameters of a number of existing algorithms. All proposed schemes extend the network lifetime and due to their lightweight architecture they are very efficient on WSN nodes with constrained resources. Hence it is recommended that a cross-layer approach should be a feature of any routing algorithm for WSNs.
Apparatus and method for monitoring performance in a communications network
Abstract: A method for monitoring the performance of a media streaming service being used to deliver media streams to user equipment devices comprises the step of determining an encoding characteristic of a first media stream being monitored. A functional dependency model is established between a first System Service Key Performance Indicator, S-KPI, and one or more Resource Service Key Performance Indicators, R-KPIs, for the first media stream.
The established functional dependency model is used for monitoring a second media stream having the same encoding characteristic as the first media stream.
Service Performance in Communications Network
Abstract
A method for monitoring performance of a service delivered to user equipment devices via a communications network as perceived by a user comprising the steps of collecting Resource Service Key Performance Indicators from network resources and collecting System Service Key Performance Indicators from a representative sample of reporting user equipment devices using the service. In the next step relationship between the collected values of R-KPIs and S-KPIs is determined and then userr equipment from the representative sample is clustered. In the following step non-reporting user equipment devices are assigned to the clusters and then the method comprises collectmg R-KPIs from network resources to estimate S-KPI values based on the R-KPIs collected after determination of the relationship and the relationship.
Service Performance in Communications Network
Abstract: A method for monitoring performance of a service delivered to user equipment devices via a communications network as perceived by a user comprising the steps of collectingvResource Service Key Performance Indicators from network resources and collecting System Service Key Performance Indicators from a representative sample of reporting user equipment devices using the service. In the next step relationship between the collected values of R-KPIs and S-KPIs is determined and then user equipment from the representative sample is clustered. In the following step non-reporting user equipment devices are assigned to the clusters and then the method comprises collectmg R-KPIs from network resources to estimate S-KPI values based on the R-KPIs collected after determination of the relationship and the relationship.
Quality of service monitoring device and method of monitoring quality of service
The invention provides a quality of service monitoring device for use With a user equipment comprising a base platform and an external communication module adapted to receive from an external source one or more listener modules and/or detector modules for incorporation into the base platform. The base platform is coupled to the external communication module to receive one or more listener modules and detector modules and is adapted to install received modules to extend the functionality of the base platform so as to perform the functionality of the installed modules. A corresponding method is provided. There is also provided a quality of service monitoring apparatus, for use on the network side, as well as a corresponding method.
A method of automatic assessment of feature compatibility in mobile networks
Abstract: Deployment and upgrade of a mobile network have always been challenging tasks. Very often they require human intervention because telecom networks are complex systems composed of different nodes that need to be compatible in order to communicate and provide network services. Therefore in current telecommunication systems a network expert must check all the requirements and compatibilities of the network prior to activation of a new service. Automation of the assessment of network compatibility is one of the key enablers for Autonomic Management of telecom networks. In this paper we describe a new method for automatic end-to-end assessment of compatibility between network features in a telecom network. The method enables fast, easy and accurate decision making regarding the planning of new feature deployment or the upgrade of already existing features. We built a prototype that demonstrates the described method. It shows that our method is not bound to any type of telecom network and could be used to automate deployment or upgrade of a multiple-domain network.
Energy optimisation of protocols in Wireless Sensor Network: Novel cross-layer techniques to expand system lifetime
Recent technological developments in embedded systems have led to the emergence of a new class of networks, known as Wireless Sensor Networks (WSNs), where individual nodes cooperate wirelessly with each other with the goal of sensing and interacting with the environment. Routing has a significant influence on the overall WSN lifetime, and providing an energy efficient routing protocol remains an open problem. This thesis addresses the issue of designing WSN routing methods that feature energy efficiency. This book presents novel cross-layer routing algorithms. It also investigates the impact of the hop distance on network lifetime and proposes a method of determining the optimal location of the relay node. The problem of predicting the transition region (the zone separating the region where all packets can be received and that where no data can be received) is also addressed. The performance of described solutions was tested in simulations and also they were deployed on sensor nodes using TinyOS. All proposed schemes extend the network lifetime and due to their lightweight architecture they are very efficient WSN nodes with constrained resources.
Synchronization service integrated into routing layer in wireless sensor networks
Abstract—The time synchronization problem needs to be considered in a distributed system. In Wireless Sensor Networks (WSNs) this issue must be solved with limited computational, communication and energy resources. Many synchronization protocols exist for WSNs. However, in most cases these protocols are independent entities with specific packets, communication scheme and network hierarchy. This solution is not energy efficient. Because it is very rare for synchronization not to be necessary in WSNs, we advocate integrating the synchronization service into the routing layer.We have implemented this approach in a new synchronization protocol called Routing Integrated Synchronization Service (RISS). Our tests show that RISS is very time and energy efficient and also is characterized by a small overhead. We have compared its performance experimentally to that of the FTSP synchronization protocol and it has proved to offer better time precision than the latter protocol.
Cross-layer energy optimisation of routing protocols in Wireless Sensor Networks
Abstract: Recent technological developments in embedded systems have led to the emergence of a new class of networks, known as Wireless Sensor Networks (WSNs), where individual nodes cooperate wirelessly with each other with the goal of sensing and interacting with the environment.Many routing protocols have been developed to meet the unique and challenging characteristics of WSNs
(notably very limited power resources to sustain an expected lifetime of perhaps years, and the restricted computation, storage and communication capabilities of nodes that are nonetheless required to support large networks and diverse applications). No standards for routing have been developed yet for WSNs, nor has any protocol gained a dominant position among the research community. Routing has a significant influence on the overall WSN lifetime, and providing an energy efficient routing protocol remains an open problem. This thesis addresses the issue of designing WSN routing methods that feature energy efficiency.
A common time reference across nodes is required in most WSN applications. It is needed, for example, to time-stamp sensor samples and for duty cycling of nodes. Also many routing protocols require that nodes communicate according to some predefined schedule. However, independent distribution of the time information, without considering the routing algorithm schedule or network topology may lead to a failure of the synchronisation protocol. This was confirmed empirically, and was shown to result in loss of connectivity. This can be avoided by integrating the synchronisation service into the network layer with a so-called cross-layer approach. This approach introduces interactions between the layers of a conventional layered network stack, so that the routing layer may share information with other layers. I explore whether energy efficiency can be enhanced through the use of cross-layer optimisations and present three novel cross-layer
routing algorithms. The first protocol, designed for hierarchical, cluster based networks and called CLEAR (Cross Layer Efficient Architecture for Routing), uses the routing algorithm to distribute time information which can be used for efficient duty cycling of nodes. The second method – called RISS (Routing Integrated Synchronization Service) – integrates time synchronization into the network layer and is designed to work well in flat, non-hierarchical network topologies. The third method – called SCALE (Smart Clustering Adapted LEACH) – addresses
the influence of the intra-cluster topology on the energy dissipation of nodes. I also investigate the impact of the hop distance on network lifetime and propose a method of determining the optimal location of the relay node (the node through which data is routed in a two-hop network). I also address the problem of predicting the transition region (the zone separating the region where all packets can be received and that where no data can be received) and I describe a way of preventing the forwarding of packets through relays belonging in this transition region.
I implemented and tested the performance of these solutions in simulations and also deployed these routing techniques on sensor nodes using TinyOS. I compared the average power consumption of the nodes and the precision of time synchronization with the corresponding parameters of a number of existing algorithms. All proposed schemes extend the network lifetime and due to their
lightweight architecture they are very efficient on WSN nodes with constrained resources. Hence it is recommended that a cross-layer approach should be a feature of any routing algorithm for WSNs.
Reception region characterisation using a 2.4 GHz direct sequence spread spectrum radio
On the problem of energy efficiency of multi-hop vs one-hop routing in Wireless Sensor Networks
Abstract: The hop distance strategy in Wireless Sensor Networks (WSNs) has a major impact on energy consumption of each sensor mote. Long-hop routing minimizes reception cost. However, a substantial power demand is incurred for long distance transmission. Since the transceiver is the major source of power consumption in the node, optimizing the routing for hop length can extend significantly the lifetime of the network. This paper explores when multi-hop routing is more energy efficient than direct transmission to the sink and the conditions for which the two-hop strategy is optimal. Experimental evidence is provided in to support of these conclusions. The tests showed that the superiority of the multi-hop scheme depends on the source-sink distance
and reception cost. They also demonstrated that the two-hop strategy is most energy efficient when the relay is at the midpoint of the total transmission radius. Our results may be used in existing routing protocols to select optimal relays or to determine whether it is better to send packets directly to the base station or through intermediate nodes.
Cross-Layer Routing with Data Delivery Guarantee in Wireless Sensor Networks
Abstract: A common time reference across nodes is required in most Wireless Sensor Networks (WSNs) applications. It is needed, for example, to time-stamp sensor samples and for long-term duty cycling of nodes. Also many routing protocols require that nodes communicate according to some predefined schedule for reasons of energy eficiency. However, independent distribution of the time information, without considering the routing algorithm schedule or network topology may lead to a failure of the synchronisation protocol. This was confirmed empirically, and was shown to result in loss of connectivity. This can be avoided by integrating the synchronisation service into the network layer with a so-called cross-layer approach. This approach introduces interactions between the layers of a conventional layered network stack, so that the routing layer may share information with other layers. We explore whether energy eficiency can be enhanced through the use of cross-layer optimisations and present two novel cross-layer routing algorithms. The first protocol, designed for hierarchical, cluster based networks and called CLEAR (Cross Layer Eficient Architecture for Routing), uses the routing algorithm to distribute time information which can be used for eficient duty cycling of nodes. The second method – called RISS (Routing Integrated Synchronization Service) – integrates time synchronization into the network layer and is designed to work well in flat, non-hierarchical network topologies.
We implemented and tested the performance of these solutions in simulations and also deployed these routing techniques on sensor nodes using TinyOS. We compared the average power consumption of the nodes and the precision of time synchronization with the corresponding parameters of a number of existing algorithms. All proposed schemes extend the network lifetime and due to their lightweight architecture they are very eficient on WSN nodes with constrained resources. Hence it is recommended that a cross-layer approach should be a feature of any routing algorithm for WSNs.