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.

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 co-located 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.

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 SCUBA1project. 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.

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.

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 specifications 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.

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.

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.

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.