Nanopore sequencing offers promising potential for rapid and target-agnostic sensing and diagnostics in field applications. However, realizing this potential necessitates addressing not only sample collection and processing challenges but also concerns related to information security and privacy when deployed in field devices. We thus introduce the Secure Bloom-Filter Analysis and Compression (SB-FAC) architecture, which breaks bioinformatics computations into a Bloom-filter-based field preprocessing stage that identifies regions of interest in the raw read data and a server-side interpretation stage that combines and interprets these identified regions. Sensitive information encoded in the Bloom filter can be protected from extraction by the use of a cryptographic hash paired with a salt in a Trusted Platform Module (TPM). We experimentally validate the predicted scaling of this approach, confirming that cost per operation linear in salt length, while reverse engineering cost is exponential.

Laboratory protocols are critical to biological research and development, yet difficult to communicate and reproduce across projects, investigators, and organizations. While many attempts have been made to address this challenge, there is currently no available protocol representation that is unambiguous enough for precise interpretation and automation, yet simultaneously “human friendly” and abstract enough to enable reuse and adaptation. The Laboratory Open Protocol language (LabOP) is a free and open protocol representation aiming to address this gap, building on a foundation of UML, Autoprotocol, Aquarium, SBOL RDF, and the Provenance Ontology. LabOP provides a linked-data representation both for protocols and for records of their execution and the resulting data, as well as a framework for exporting from LabOP for execution by either humans or laboratory automation. LabOP is currently implemented in the form of an RDF knowledge representation, specification document, and Python library, and supports execution as manual “paper protocols,” by Autoprotocol or by Opentrons. From this initial implementation, LabOP is being further developed as an open community effort.

Opportunistic managed access to local in-network compute resources can improve the performance of distributed applications and reduce the dependence on shared network resources. Instead of backhauling application data to a centralized cloud data center for processing, networked services may be adaptively and continuously dispersed into shared compute resources that are closer to the source of need. While this approach has several benefits, support for mission-aware access to computation is often an afterthought, and is implemented as a brittle extension over traditional load-balancer solutions.In this work, we investigate the design of two priority-aware resource allocation strategies and two load-balancing dispatching strategies as first class citizens in an open-source dispersed computing middleware. We present a control theoretic analysis of these load-balancing primitives to identify weaknesses and strengths in our design, and recommend future directions. In parallel, we prototype two priority-aware allocation algorithms to validate our priority predictions. In initial experiments our prototype shows substantial gains in processing prioritized load. Finally, we make our source-code and experimental configurations open source.

The complexity of large-scale distributed systems, particularly when deployed in physical space, calls for new mechanisms to address composability and reusability of collective adaptive behaviour. Computational fields have been proposed as an effective abstraction to fill the gap between the macro-level of such systems (specifying a system’s collective behaviour) and the micro-level (individual devices’ actions of computation and interaction to implement that collective specification), thereby providing a basis to better facilitate the engineering of collective APIs and complex systems at higher levels of abstraction. This article proposes a full formal foundation for field computations, in terms of a core (higher-order) calculus of computational fields containing a few key syntactic constructs, and equipped with typing, denotational and operational semantics. Critically, this allows formal establishment of a link between the micro- and macro-levels of collective adaptive systems by a result of computational adequacy and abstraction for the (aggregate) denotational semantics with respect to the (per-device) operational semantics.

A wide variety of Artificial Intelligence (AI) techniques, from expert systems to machine learning to robotics, are needed in the field of synthetic biology. This paper describes the design-build-test engineering cycle and lists some challenges in which AI can help.

Airborne sensor platforms are becoming increasingly significant for both civilian and military operations; yet, at present, their sensors are typically idle for much of their flight time, e.g., while the sensor-equipped platform is in transit to and from the locations of sensing tasks. The sensing needs of many other potential information consumers might thus be served by sharing such sensors, thereby allowing other information consumers to opportunistically task them during their otherwise unscheduled time, as well as enabling other improvements, such as decreasing the number of platforms needed to achieve a goal and increasing the resilience of sensor tasks through duplication. We have implemented a prototype system realizing these goals in Mission-Driven Tasking of Information Producers (MTIP), which leverages an agent-based representation of tasks and sensors to enable fast, effective, and adaptive opportunistic sharing of airborne sensors. Using a simulated large-scale disaster-response scenario populated with publicly available Geographic Information System (GIS) datasets, we demonstrate that correlations in task location are likely to lead to a high degree of potential for sensor-sharing. We then validate that our implementation of MTIP can successfully carry out such sharing, showing that it increases the number of sensor tasks served, reduces the number of platforms required to serve a given set of sensor tasks, and adapts well to radical changes in flight path.

A key problem when coordinating the behaviour of spatially situated networks, like those typically found in the Internet of Things (IoT), is adaptation to changes impacting network topology, density, and heterogeneity. Computational goals for such systems, however, are often dependent on geometric properties of the continuous environment in which the devices are situated rather than the particulars of how devices happen to be distributed through it. In this article, we identify a new property of distributed algorithms, eventual consistency , which guarantees that computation converges to a final state that approximates a predictable limit, based on the continuous environment, as the density and speed of devices increases. We then identify a large class of programs that are eventually consistent, building on prior results on the field calculus computational model (Beal et al. 2015; Viroli et al. 2015a) that identify a class of self-stabilizing programs. Finally, we confirm through simulation of IoT application scenarios that eventually consistent programs from this class can provide resilient behavior where programs that are only converging fail badly.

Airborne sensors are often idle for much of their flight, e.g., while the platform carrying them is in transit to and from the locations of sensor tasks. The sensing needs of many other potential information consumers might thus be served by sharing such sensors, allowing other information consumers to opportunistically task them during their otherwise unscheduled time. Toward this end, we have developed Mission-Driven Tasking of Information Producers (MTIP), a prototype system for opportunistic sharing of airborne sensors. This paper describes its implementation as an agent-based task allocation system on top of the Marti Quality of Service (QoS)-managed publish-subscribe information management system, and presents simulations of a disaster response scenario demonstrating how MTIP can increase the number of sensor tasks served as well as reducing the number of UAVs required to serve a given set of sensor tasks.

A number of recent works have investigated the notion of “computational fields” as a means of coordinating systems in distributed, dense and dynamic environments such as pervasive computing, sensor networks, and robot swarms. We introduce a minimal core calculus meant to capture the key ingredients of languages that make use of computational fields: functional composition of fields, functions over fields, evolution of fields over time, construction of fields of values from neighbours, and restriction of a field computation to a sub-region of the network. We formalise a notion of type soundness for the calculus that encompasses the concept of domain alignment, and present a sound static type inference system. This calculus and its type inference system can act as a core for actual implementation of coordination languages and models, as well as to pave the way towards formal analysis of properties concerning expressiveness, self-stabilisation, topology independence, and relationships with the continuous space–time semantics of spatial computations.

A common swarm task is to disperse evenly through an environment from an initial tightly packed formation. Due to communication and sensing limitations, it is often necessary to execute this task with little or no communication between swarm members. Unfortunately, prior approaches based on repulsive forces or uniform random walks can often converge quite slowly. With an appropriate choice of random distribution, however, it is possible to generate optimal or near-optimal dispersion and mixing in swarms with zero communication. In particular, we discuss three extremely simple algorithms: reactive Levy walk, reactive ball dispersion, and purely reactive dispersion. All three algorithms vastly outperform prior approaches in both constrained and unconstrained environments, providing a range of options for trading off between aggressiveness and evenness in dispersion.

The complexity and tight integration of electromechanical systems often makes them "brittle" and hard to modify in response to changing requirements. We aim to remedy this by capturing expert knowledge as functional blueprints, an idea inspired by regulatory processes that occur in natural morphogenesis. We then apply this knowledge in an intelligent design variation tool. When a user modifies a design, our tool uses functional blueprints to modify other components in response, thereby maintaining integration and reducing the need for costly search or constraint solving. In this paper, we refine the functional blueprint concept and discuss practical issues in applying it to electromechanical systems. We then validate our approach with a case study applying our prototype tool to create variants of a miniDroid robot and by empirical evaluation of convergence dynamics of networks of functional blueprints.

Jacob Beal, Stefan Dulman, Olivier Michel, Antoine Spicher; Editorial, The Computer Journal, Volume 56, Issue 12, 1 December 2013, Pages 1397–1398, https://doi.

In an environment increasingly saturated with computing devices, it is desirable for some services to be distributed, executing via local interactions between devices. Creating fast, flexible and dynamic distributed services requires a general model of function calls distributed over space–time. Prior models, however, have either depended strongly on large-scale Internet infrastructure or have restrictions in the scope or resolution of space–time for inputs, outputs or evaluation of the function. We address this by providing a formal general model of function calls over space–time. We then fully realize a practical model of space–time function calls, based in the Proto language and present both theoretical and empirical results. Finally, we show how our results for Proto generalize into implications for any model of distributed computing.

Many natural organisms exhibit canalization: small genetic changes are accommodated by adaptation in other systems that interact with them. Engineered systems, however, are typically quite brittle, making design automation extremely difficult. We propose to address this problem with a generative representation of design based on manifold operators. The operator set we propose combines the intuitive simplicity of top-down rewrite rules with the flexibility and distortion tolerance of bottom-up GRN-based models. An embryogeny specified using this representation thus places constraints on a developing design, rather than specifying a fixed body plan, allowing canalization processes to modulate the design as it continues to develop. We demonstrate our ideas in the domain of electromechanical design and validate them with simulations at different levels of abstraction.

research-article Share on Spatial Computing: Distributed Systems That Take Advantage of Our Geometric World Authors: Jacob Beal BBN Technologies BBN TechnologiesView Profile , Olivier Michel University of Paris 12 University of Paris 12View Profile , Ulrik Pagh Schultz University of Southern Denmark University of Southern DenmarkView Profile Authors Info & Claims ACM Transactions on Autonomous and Adaptive SystemsVolume 6Issue 2Article No.: 11pp 1–3https://doi.org/10.1145/1968513.1968514Published:01 June 2011Publication History 12citation386DownloadsMetricsTotal Citations12Total Downloads386Last 12 Months14Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access