Panayiotis Andreou

Ditis: A Collaborative Virtual Medical Team for Home Healthcare of Cancer Patients

Complex and chronic illnesses, such as cancer, demand the use of specialised treatment protocols, administered and monitored by a patient centric co-ordinated team of multidisciplinary healthcare professionals. Care of chronic illnesses (e.g. cancer patients) by a team of health care professionals at home is often necessary due to the protracted length of the illness, the differing medical conditions, as well as the different stages of the chronic illness. Most importantly home care can offer comfort for the patient and their family, in the familiar surrounding of their home, and at the same time being cost effective, as compared to the high cost of hospital beds. Hospital based treatment for chronic patients is limited, often demand based for short periods of time, used mainly for acute incidents. As it is not possible for the health care team to be physically present by the patient at all times, or at any time physically together, whilst the patient is undergoing treatment at home (or work), a principal aim is to overcome the difficulty of coordination and communication, through DITIS (ΔΙΤΗΣ, in Greek, stands for: Network for Medical Collaboration). DITIS is a system that supports dynamic Virtual Collaborative HealthCare Teams dealing with the home-healthcare. It supports the dynamic creation, management and co-ordination of virtual medical teams, for the continuous treatment of the patient at home, and if needed for periodic visits to places of specialised treatment and back home.

Optimized query routing trees for wireless sensor networks

In order to process continuous queries over Wireless Sensor Networks (WSNs), sensors are typically organized in a Query Routing Tree (denoted as T) that provides each sensor with a path over which query results can be transmitted to the querying node. We found that current methods deployed in predominant data acquisition systems construct T in a sub-optimal manner which leads to significant waste of energy. In particular, since T is constructed in an ad hoc manner there is no guarantee that a given query workload will be distributed equally among all sensors. That leads to data collisions which represent a major source of energy waste. Additionally, current methods only provide a topological-based method, rather than a query-based method, to define the interval during which a sensing device should enable its transceiver in order to collect the query results from its children. We found that this imposes an order of magnitude increase in energy consumption. In this paper we present MicroPulse^+, a novel framework for minimizing the consumption of energy during data acquisition in WSNs. MicroPulse^+ continuously optimizes the operation of T by eliminating data transmission and data reception inefficiencies using a collection of in-network algorithms. In particular, MicroPulse^+ introduces: (i) the Workload-Aware Routing Tree (WART) algorithm, which is established on profiling recent data acquisition activity and on identifying the bottlenecks using an in-network execution of the critical path method; and (ii) the Energy-driven Tree Construction (ETC) algorithm, which balances the workload among nodes and minimizes data collisions. We show through micro-benchmarks on the CC2420 radio chip and trace-driven experimentation with real datasets from Intel Research and UC-Berkeley that MicroPulse^+ provides significant energy reductions under a variety of conditions thus prolonging the longevity of a wireless sensor network.

MINT Views: Materialized In-Network Top-k Views in Sensor Networks

In this paper we introduce MINT (materialized in-network top-k) Views, a novel framework for optimizing the execution of continuous monitoring queries in sensor networks. A typical materialized view V maintains the complete results of a query Q in order to minimize the cost of future query executions. In a sensor network context, maintaining consistency between V and the underlying and distributed base relation R is very expensive in terms of communication. Thus, our approach focuses on a subset V(sube. V) that unveils only the k highest-ranked answers at the sink for some user defined parameter k. We additionally provide an elaborate description of energy-conscious algorithms for constructing, pruning and maintaining such recursively- defined in-network views. Our trace-driven experimentation with real datasets show that MINT offers significant energy reductions compared to other predominant data acquisition models.

ETC: Energy-Driven Tree Construction in Wireless Sensor Networks

Continuous queries in Wireless Sensor Networks (WSNs) are founded on the premise of Query Routing Tree structures (denoted as T), which provide sensors with a path to the querying node. Predominant data acquisition systems for WSNs construct such structures in an ad-hoc manner and therefore there is no guarantee that a given query workload will be distributed equally among all sensors. That leads to data collisions which represent a major source of energy waste. In this paper we present the Energy-driven Tree Construction (ETC) algorithm, which balances the workload among nodes and minimizes data collisions, thus reducing energy consumption, during data acquisition in WSNs. We show through real micro-benchmarks on the CC2420 radio chip and trace-driven experimentation with real datasets from Intel Research and UC-Berkeley that ETC can provide significant energy reductions under a variety of conditions prolonging the longevity of a wireless sensor network.

KSpot: Effectively Monitoring the K Most Important Events in a Wireless Sensor Network

This demo presents a graphical user interface and ranking system, coined KSpot, for effectively monitoring the K highest-ranked answers to a query Q in a Wireless Sensor Network. KSpot deploys state-of-the-art distributed Top-k query processing algorithms in order to realize both snapshot queries and historic queries minimizing the consumption of system resources and prolonging the lifetime of the deployed sensor network. Additionally, KSpot is user-friendly and customizable featuring an intuitive user interface that enables a user to express declarative SQL-like queries over any ad-hoc scenario and to display the results graphically as opposed to the traditional tabular representation. To demonstrate the applicability of our system during the conference, we will continuously identify the K conference rooms with the highest sound level and display them such that conference attendees will be able to quickly determine the rooms with the most active discussions. The demo will also allow attendees to customize the system by changing the target scenario (e.g., by adapting the K value, the sensed parameter, etc.) Finally, we will present KSpots system panel which continuously displays the savings in energy and messages that our system yields.

SenseSwarm: a perimeter-based data acquisition framework for mobile sensor networks

This paper assumes a set of n mobile sensors that move in the Euclidean plane as a swarm. Our objectives are to explore a given geographic region by detecting and aggregating spatio-temporal events of interest and to store these events in the network until the user requests them. Such a setting finds applications in environments where the user (i.e., the sink) is infrequently within communication range from the field deployment. Our framework, coined SenseSwarm, dynamically partitions the sensing devices into perimeter and core nodes. Data acquisition is scheduled at the perimeter in order to minimize energy consumption while storage and replication takes place at the core nodes which are physically and logically shielded to threats and obstacles. To efficiently identify the perimeter of the swarm we devise the Perimeter Algorithm (PA), an efficient distributed algorithm with a message complexity of O(p + n), where p denotes the number of nodes on the perimeter and n the overall number of nodes. For storage and replication we devise a spatio-temporal in-network aggregation scheme based on minimum bounding rectangles and minimum bounding cuboids. Our trace-driven experimentation shows that our framework can offer significant energy reductions while maintaining high data availability rates.

The MicroPulse Framework for Adaptive Waking Windows in Sensor Networks

In this paper we present MicroPulse, a novel framework for adapting the waking window of a sensing device S based on the data workload incurred by a query Q. Assuming a typical tree-based aggregation scenario, the waking window is defined as the time interval r during which S enables its transceiver in order to collect the results from its children. Minimizing the length of r enables S to conserve energy that can be used to prolong the longevity of the network and hence the quality of results. Our method is established on profiling recent data acquisition activity and on identifying the bottlenecks using an in-network execution of the Critical Path Method. We show through trace- driven experimentation with a real dataset that MicroPulse can reduce the energy cost of the waking window by three orders of magnitude.

Workload-Aware Query Routing Trees in Wireless Sensor Networks

Continuous queries in wireless sensor networks are established on the premise of a routing tree that provides each sensor with a path over which answers can be transmitted to the query processor. We found that these structures are sub- optimality constructed in predominant data acquisition systems leading to an enormous waste of energy. In this paper we present MicroPulse1, a workload-aware optimization algorithm for query routing trees in wireless sensor networks. Our algorithm is established on profiling recent data acquisition activity and on identifying the bottlenecks using an in-network execution of the critical path method. A node S utilizes this information in order to locally derive the time instance during which it should wake up, the interval during which it should deliver its workload and the workload increase tolerance of its parent node. We additionally provide an elaborate description of energy-conscious algorithms for disseminating and maintaining the critical path cost in a distributed manner. Our trace-driven experimentation with real sensor traces from Intel Research Berkeley shows that MicroPulse can reduce the data acquisition costs by many orders.

Multi-objective Query Optimization in Smartphone Social Networks

The bulk of social network applications for smart phones (e.g., Twitter, Face book, Foursquare, etc.) currently rely on centralized or cloud-like architectures in order to carry out their data sharing and searching tasks. Unfortunately, the given model introduces both data-disclosure concerns (e.g., disclosing all captured media to a central entity) and performance concerns (e.g., consuming precious smart phone battery and bandwidth during content uploads). In this paper, we present a novel framework, coined Smart Opt, for searching objects (e.g., images, videos, etc.) captured by the users in a mobile social community. Our framework, is founded on an in-situ data storage model, where captured objects remain local on their owners smart phones and searches then take place over a novel lookup structure we compute dynamically, coined the Multi-Objective Query Routing Tree (MO-QRT). Our structure concurrently optimizes several conflicting objectives (i.e., it minimizes energy consumption, minimizes search delay and maximizes query recall), using a Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) that calculates a diverse set of high quality non-dominated solutions in a single run. We assess our ideas with mobility patterns derived by Microsofts Geolife project and social patterns derived by DBLP. Our study reveals that Smart Opt can yield query recall rates of 95%, with one order of magnitude less time and two orders of magnitude less energy than its competitors.

FSort: external sorting on flash-based sensor devices

In long-term deployments of Wireless Sensor Networks, it is often more efficient to store sensor readings locally at each device and transmit those readings to the user only when requested (i.e., in response to a user query). Many of the techniques that collect information from a sensor network require that the data is sorted on some attribute (e.g., range queries, top-k queries, join queries, etc.) Yet, the underlying storage medium of these devices (i.e., Flash media) presents some unique characteristics which renders traditional disk-based sorting algorithms inefficient in this context. In this paper we devise the FSort algorithm, an efficient external sorting algorithm for flash-based sensor devices with a small memory footprint. FSort minimizes the expensive write/delete operations of flash memory minimizing in that way the consumption of energy. In particular, FSort uses a top-down replacement selection algorithm in order to produce sorted runs on flash media in a log-based manner. Sorted runs are then recursively merged in order to yield the sorted result. Our experimentation with real traces from Intel Research Berkeley show that FSort greatly outperforms the traditional External Mergesort Algorithm both in regards to time and energy consumption. We found similar advantages in regards to the wearability constraints of flash media.