Agimone: Middleware Support for Seamless Integration of Sensor and IP Networks

The scope of wireless sensor network (WSN) applications has traditionally been restricted by physical sensor coverage and limited computational power. Meanwhile, IP networks like the Internet offer tremendous connectivity and computing resources. This paper presents Agimone, a middleware layer that integrates sensor and IP networks as a uniform platform for flexible application deployment. This layer allows applications to be deployed on the WSN in the form of mobile agents which can autonomously discover and migrate to other WSNs, using a common IP backbone as a bridge. It facilitates data sharing between WSNs and the IP network through remote tuple space operations, allowing sensors to easily defer expensive computations to more-powerful devices. We demonstrate the expressiveness of Agimone’s programming model by examining a prototype cargotracking application that has been deployed using this system. We also provide an empirical evaluation of Agimone using a series of benchmarks deployed on two WSNs consisting of MICA2 sensor nodes connected by an IP network. These benchmarks show that inter-network tuple space operations take 10ms, and that one-way agent migrations between two different WSNs take approximately 83ms

Tuple Space Coordination Across Space and Time

Abstract. CAST is a coordination model designed to support interactions among agents executing on hosts that make up a mobile ad hoc network (MANET). From an application programmer’s point of view, CAST makes it possible for operations to be executed at arbitrary locations in space, at prescribed times which may be in the future, and on remote hosts even when no end-to-end connected route exists between the initiator and target(s) of the operation. To accomplish this, CAST assumes that each host moves in space in accordance with a motion profile which is accurate but which at any given time extends into the future for a limited duration. These motion profiles are freely exchanged among hosts in the network through a gossiping protocol. Knowledge about the motion profiles of the other hosts in the network allows for source routing of operation requests and replies over disconnected routes. In this paper, we present the CAST model and its formalization. We also discuss the feasibility of realizing this model.

Clustered intramammary microcalcifications not associated with a mass.

Singapore Medical Journal36129-3

Programming wireless sensor networks with the TeenyLIME middleware

Wireless sensor networks (WSNs) are evolving to support sense-andreact applications, where actuators are physically interspersed with the sensors that trigger them. This solution maximizes localized interactions, improving resource utilization and reducing latency w.r.t. solutions with a centralized sink. Nevertheless, application development becomes more complex: the control logic must be embedded in the network, and coordination among multiple tasks is needed to achieve the application goals. This paper presents TeenyLIME, a WSN middleware designed to address the above challenges. TeenyLIME provides programmers with the high-level abstraction of a tuple space, enabling data sharing among neighboring devices. These and other WSN-specific constructs simplify the development of a wide range of applications, including sense-and-react ones. TeenyLIME yields simpler, cleaner, and more reusable implementations, at the cost of only a very limited decrease in performance. We support these claims through a source-level, quantitative comparison between implementations based on TeenyLIME and on mainstream approaches, and by analyzing measures of processing overhead and power consumption obtained through cycle-accurate emulation

Self-organising Pervasive Ecosystems: A Crowd Evacuation Example

The dynamics of pervasive ecosystems are typically highly unpredictable, and therefore self-organising approaches are often exploited to make their applications resilient to changes and failures. The SAPERE approach we illustrate in this paper aims at addressing this issue by taking inspiration from natural ecosystems, which are regulated by a limited set of "laws" evolving the population of individuals in a self-organising way. Analogously, in our approach, a set of so-called eco-laws coordinate the individuals of the pervasive computing system (humans, devices, signals), in a way that is shown to be expressive enough to model and implement interesting real-life scenarios. We exemplify the proposed framework discussing a crowd evacuation application, tuning and validating it by simulation

Characterization of the human myelin oligodendrocyte glycoprotein antibody response in demyelination

Over recent years, human autoantibodies targeting myelin oligodendrocyte glycoprotein (MOG Ab) have been associated with monophasic and relapsing central nervous system demyelination involving the optic nerves, spinal cord, and brain. While the clinical relevance of MOG Ab detection is becoming increasingly clear as therapeutic and prognostic differences from multiple sclerosis are acknowledged, an in-depth characterization of human MOG Ab is required to answer key challenges in patient diagnosis, treatment, and prognosis. Herein, we investigated the epitope, binding sensitivity, and affinity of MOG Ab in a cohort of 139 and 148 MOG antibody-seropositive children and adults (n = 287 patients at baseline, 130 longitudinal samples, and 22 cerebrospinal fluid samples). MOG extracellular domain was also immobilized to determine the affinity of MOG Ab. MOG Ab response was of immunoglobulin G1 isotype, and was of peripheral rather than intrathecal origin. High affinity MOG Ab were detected in 15% paediatric and 18% adult sera. More than 75% of paediatric and adult MOG Ab targeted a dominant extracellular antigenic region around Proline42. MOG Ab titers fluctuated over the progression of disease, but affinity and reactivity to Proline42 remained stable. Adults with a relapsing course intrinsically presented with a reduced immunoreactivity to Proline42 and had a more diverse MOG Ab response, a feature that may be harnessed for predicting relapse. Higher titers of MOG Ab were observed in more severe phenotypes and during active disease, supporting the pathogenic role of MOG Ab. Loss of MOG Ab seropositivity was observed upon conformational changes to MOG, and this greatly impacted the sensitivity of the detection of relapsing disorders, largely considered as more severe. Careful consideration of the binding characteristics of autoantigens should be taken into account when detecting disease-relevant autoantibodies.Fiona Tea, Joseph A. Lopez, Sudarshini Ramanathan, Vera Merheb, Fiona X. Z. Lee, Alicia Zou ... et al. (Australasian and New Zealand MOG Study Group