Middleware for Mobile Sensing Applications in Urban Environments

Sensor networks represent an attractive tool to observe the physical world. Networks of tiny sensors can be used to detect a fire in a forest, to monitor the level of pollution in a river, or to check on the structural integrity of a bridge. Application-specific deployments of static-sensor networks have been widely investigated. Commonly, these networks involve a centralized data-collection point and no sharing of data outside the organization that owns it. Although this approach can accommodate many application scenarios, it significantly deviates from the pervasive computing vision of ubiquitous sensing where user applications seamlessly access anytime, anywhere data produced by sensors embedded in the surroundings. With the ubiquity and ever-increasing capabilities of mobile devices, urban environments can help give substance to the ubiquitous sensing vision through Urbanets, spontaneously created urban networks. Urbanets consist of mobile multi-sensor devices, such as smart phones and vehicular systems, public sensor networks deployed by municipalities, and individual sensors incorporated in buildings, roads, or daily artifacts. My thesis is that "multi-sensor mobile devices can be successfully programmed to become the underpinning elements of an open, infrastructure-less, distributed sensing platform that can bring sensor data out of their traditional close-loop networks into everyday urban applications". Urbanets can support a variety of services ranging from emergency and surveillance to tourist guidance and entertainment. For instance, cars can be used to provide traffic information services to alert drivers to upcoming traffic jams, and phones to provide shopping recommender services to inform users of special offers at the mall. Urbanets cannot be programmed using traditional distributed computing models, which assume underlying networks with functionally homogeneous nodes, stable configurations, and known delays. Conversely, Urbanets have functionally heterogeneous nodes, volatile configurations, and unknown delays. Instead, solutions developed for sensor networks and mobile ad hoc networks can be leveraged to provide novel architectures that address Urbanet-specific requirements, while providing useful abstractions that hide the network complexity from the programmer. This dissertation presents two middleware architectures that can support mobile sensing applications in Urbanets. Contory offers a declarative programming model that views Urbanets as a distributed sensor database and exposes an SQL-like interface to developers. Context-aware Migratory Services provides a client-server paradigm, where services are capable of migrating to different nodes in the network in order to maintain a continuous and semantically correct interaction with clients. Compared to previous approaches to supporting mobile sensing urban applications, our architectures are entirely distributed and do not assume constant availability of Internet connectivity. In addition, they allow on-demand collection of sensor data with the accuracy and at the frequency required by every application. These architectures have been implemented in Java and tested on smart phones. They have proved successful in supporting several prototype applications and experimental results obtained in ad hoc networks of phones have demonstrated their feasibility with reasonable performance in terms of latency, memory, and energy consumption.

Optimisation Problems in Wireless Sensor Networks : Local Algorithms and Local Graphs

This thesis studies optimisation problems related to modern large-scale distributed systems, such as wireless sensor networks and wireless ad-hoc networks. The concrete tasks that we use as motivating examples are the following: (i) maximising the lifetime of a battery-powered wireless sensor network, (ii) maximising the capacity of a wireless communication network, and (iii) minimising the number of sensors in a surveillance application. A sensor node consumes energy both when it is transmitting or forwarding data, and when it is performing measurements. Hence task (i), lifetime maximisation, can be approached from two different perspectives. First, we can seek for optimal data flows that make the most out of the energy resources available in the network; such optimisation problems are examples of so-called max-min linear programs. Second, we can conserve energy by putting redundant sensors into sleep mode; we arrive at the sleep scheduling problem, in which the objective is to find an optimal schedule that determines when each sensor node is asleep and when it is awake. In a wireless network simultaneous radio transmissions may interfere with each other. Task (ii), capacity maximisation, therefore gives rise to another scheduling problem, the activity scheduling problem, in which the objective is to find a minimum-length conflict-free schedule that satisfies the data transmission requirements of all wireless communication links. Task (iii), minimising the number of sensors, is related to the classical graph problem of finding a minimum dominating set. However, if we are not only interested in detecting an intruder but also locating the intruder, it is not sufficient to solve the dominating set problem; formulations such as minimum-size identifying codes and locating dominating codes are more appropriate. This thesis presents approximation algorithms for each of these optimisation problems, i.e., for max-min linear programs, sleep scheduling, activity scheduling, identifying codes, and locating dominating codes. Two complementary approaches are taken. The main focus is on local algorithms, which are constant-time distributed algorithms. The contributions include local approximation algorithms for max-min linear programs, sleep scheduling, and activity scheduling. In the case of max-min linear programs, tight upper and lower bounds are proved for the best possible approximation ratio that can be achieved by any local algorithm. The second approach is the study of centralised polynomial-time algorithms in local graphs these are geometric graphs whose structure exhibits spatial locality. Among other contributions, it is shown that while identifying codes and locating dominating codes are hard to approximate in general graphs, they admit a polynomial-time approximation scheme in local graphs.

Viivesietoisten verkkojen nykytila ja tulevaisuuden haasteet

Delay and disruption tolerant networks (DTNs) are computer networks where round trip delays and error rates are high and disconnections frequent. Examples of these extreme networks are space communications, sensor networks, connecting rural villages to the Internet and even interconnecting commodity portable wireless devices and mobile phones. Basic elements of delay tolerant networks are a store-and-forward message transfer resembling traditional mail delivery, an opportunistic and intermittent routing, and an extensible cross-region resource naming service. Individual nodes of the network take an active part in routing the traffic and provide in-network data storage for application data that flows through the network. Application architecture for delay tolerant networks differs also from those used in traditional networks. It has become feasible to design applications that are network-aware and opportunistic, taking an advantage of different network connection speeds and capabilities. This might change some of the basic paradigms of network application design. DTN protocols will also support in designing applications which depend on processes to be persistent over reboots and power failures. DTN protocols could also be applicable to traditional networks in cases where high tolerance to delays or errors would be desired. It is apparent that challenged networks also challenge the traditional strictly layered model of network application design. This thesis provides an extensive introduction to delay tolerant networking concepts and applications. Most attention is given to challenging problems of routing and application architecture. Finally, future prospects of DTN applications and implementations are envisioned through recent research results and an interview with an active researcher of DTN networks.

Evaluation of regional and distant metastases in head and neck squamous cell carcinoma patients with special reference to the assessment of regional lymph nodes in oral cancer

For optimal treatment planning, a thorough assessment of the metastatic status of mucosal squamous cell carcinoma of the head and neck (HNSCC) is required. Current imaging methods do not allow the recognition of all patients with metastatic disease. Therefore, elective treatment of the cervical lymph nodes is usually given to patients in whom the risk of subclinical metastasis is estimated to exceed 15-20%. The objective of this study was to improve the pre-treatment evaluation of patients diagnosed with HNSCC. Particularly, we aimed at improving the identification of patients who will benefit from elective neck treatment. Computed tomography (CT) of the chest and abdomen was performed prospectively for 100 patients diagnosed with HNSCC. The findings were analysed to clarify the indications for this examination in this patient group. CT of the chest influenced the treatment approach in 3% of patients, while CT of the abdomen did not reveal any significant findings. Our results suggest that CT of the chest and abdomen is not indicated routinely for patients with newly diagnosed HNSCC but can be considered in selected cases. Retrospective analysis of 80 patients treated for early stage squamous cell carcinoma of the oral tongue was performed to investigate the potential benefits of elective neck treatment and to examine whether histopathological features of the primary tumour could be used in the prediction of occult metastases, local recurrence, or/and poor survival. Patients who had received elective neck treatment had significantly fewer cervical recurrences during the follow-up when compared to those who only had close observation of the cervical lymph nodes. Elective neck treatment did not result in survival benefit, however. Of the histopathological parameters examined, depth of infiltration and pT-category (representing tumour diameter) predicted occult cervical metastasis, but only the pT-category predicted local recurrence. Depth of infiltration can be used in the identification of at risk patients but no clear cut-off value separating high-risk and low-risk patients was found. None of the histopathological parameters examined predicted survival. Sentinel lymph node (SLN) biopsy was studied as a means of diagnosing patients with subclinical cervical metastases. SLN biopsy was applied to 46 patients who underwent elective neck dissection for oral squamous cell carcinoma. In addition, SLN biopsy was applied to 13 patients with small oral cavity tumours who were not intended to undergo elective neck dissection because of low risk of occult metastasis. The sensitivity of SLN biopsy for finding subclinical cervical metastases was found to be 67%, when SLN status was compared to the metastatic status of the rest of the neck dissection specimen. Of the patients not planned to have elective neck dissection, SLN biopsy revealed cervical metastasis in 15% of the patients. Our results suggest that SLN biopsy can not yet entirely replace elective neck dissection in the treatment of oral cancer, but it seems beneficial for patients with low risk of metastasis who are not intended for elective neck treatment according to current treatment protocols.

Optimisation problems in wireless sensor networks

This thesis studies optimisation problems related to modern large-scale distributed systems, such as wireless sensor networks and wireless ad-hoc networks. The concrete tasks that we use as motivating examples are the following: (i) maximising the lifetime of a battery-powered wireless sensor network, (ii) maximising the capacity of a wireless communication network, and (iii) minimising the number of sensors in a surveillance application. A sensor node consumes energy both when it is transmitting or forwarding data, and when it is performing measurements. Hence task (i), lifetime maximisation, can be approached from two different perspectives. First, we can seek for optimal data flows that make the most out of the energy resources available in the network; such optimisation problems are examples of so-called max-min linear programs. Second, we can conserve energy by putting redundant sensors into sleep mode; we arrive at the sleep scheduling problem, in which the objective is to find an optimal schedule that determines when each sensor node is asleep and when it is awake. In a wireless network simultaneous radio transmissions may interfere with each other. Task (ii), capacity maximisation, therefore gives rise to another scheduling problem, the activity scheduling problem, in which the objective is to find a minimum-length conflict-free schedule that satisfies the data transmission requirements of all wireless communication links. Task (iii), minimising the number of sensors, is related to the classical graph problem of finding a minimum dominating set. However, if we are not only interested in detecting an intruder but also locating the intruder, it is not sufficient to solve the dominating set problem; formulations such as minimum-size identifying codes and locating–dominating codes are more appropriate. This thesis presents approximation algorithms for each of these optimisation problems, i.e., for max-min linear programs, sleep scheduling, activity scheduling, identifying codes, and locating–dominating codes. Two complementary approaches are taken. The main focus is on local algorithms, which are constant-time distributed algorithms. The contributions include local approximation algorithms for max-min linear programs, sleep scheduling, and activity scheduling. In the case of max-min linear programs, tight upper and lower bounds are proved for the best possible approximation ratio that can be achieved by any local algorithm. The second approach is the study of centralised polynomial-time algorithms in local graphs – these are geometric graphs whose structure exhibits spatial locality. Among other contributions, it is shown that while identifying codes and locating–dominating codes are hard to approximate in general graphs, they admit a polynomial-time approximation scheme in local graphs.

Planar subgraphs without low-degree nodes

We study the following problem: given a geometric graph G and an integer k, determine if G has a planar spanning subgraph (with the original embedding and straight-line edges) such that all nodes have degree at least k. If G is a unit disk graph, the problem is trivial to solve for k = 1. We show that even the slightest deviation from the trivial case (e.g., quasi unit disk graphs or k = 1) leads to NP-hard problems.