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The project includes novel abstractions for sensor data aggregation and fusion performed within the network on the resource constrained devices. Enabling real-time collaboration demands lightweight, modular middleware that enables the fine-grained interactions requried by collaborative applications. We have introduced sliverware that provides extreme modularity and customizability while at the same time realizing our goal of simplifying cooperative application development.

SMASH: Secure Mobile Agent Middleware: As software components become able to move among hosts in the network, a question arises in how to secure interactions between the agents and among the agents and their host platforms. SMASH investigates the gelclair of these security requirements, provides a mobile agent architecture that embodies them, and still allows agents to move and coordinate anonymously to a limited extent.

The model includes a context specification mechanism that allows individual applications to tailor their operating contexts b complex vitamin with vitamin c their personalized needs.

The associated communication protocol, source initiated context construction, or SICC, provides b complex vitamin with vitamin c context abstraction in ad hoc networks through continuous evaluation of the context.

This relieves the application developer of the obligation of explicitly managing mobility and its implications on behavior. Weyns et al (editors), Lecture Notes in Computer Science 3374, B complex vitamin with vitamin c 2005, pp. Software: Project page b complex vitamin with vitamin c related downloads EgoSpaces: EgoSpaces is a coordination model and middleware for ad hoc mobile environments that focuses on the needs of application development in ad hoc environments by proposing an agent-centered notion of context, called a view, whose scope extends beyonr the local host to data and resources associated with hosts and agents within a subnet surrounding the agent of interest.

An agent may operate over multiple views whose definitions may change over time. An agent uses declarative specifications to constrain the contents of each view by employing a rich set of constraints that take into consideration fentanyl transdermal system of the individual data items, the agents that own them, the b complex vitamin with vitamin c on which the agents reside, and the b complex vitamin with vitamin c and logical topology of the ad hoc network.

We have formalized the concept of view, explored the notion of programming against views, discussed possible implementation strategies for transparent context maintenance, and generated a protoype system.

Choren et al (editors), Lecture Notes in Computer Science 3390, February 2005, pp. Software: Project page and related downloads Context UNITY: Context-aware computing refers to a paradigm in which applications sense aspects of the environment and use this information to adjust their behavior in response to changing circumstances.

We have created a formal model and notation (Context UNITY) for expressing quintessential aspects of context-aware computations; existential quantification, for instance, proves to be higly effective in capturing the notion of discovery in open systems. Furthermore, Context UNITY treats context in a manner that is relative to the specific needs of an individual applications and promotes an approach to context maintenance that is transparent to the application.

Home People Research Publications Links Contact. We consider a complete data life cycle, from sampling, compression, transmission to reception and decompression. Practical constraints including finite battery capacity, time-varying uplink channel and nonlinear energy harvesting model are considered.

An optimization problem is formulated in a Markov decision process framework to maximize the longterm average throughput by a hybrid of mode switching, time and power allocation, and compression ratio selection. Capitalizing on this, we first adopt value iteration (VI) algorithm to find offline optimal solution as benchmark. Then, we propose Q-learning (QL) and deep Q-learning (DQL) algorithms to obtain online solutions without prior information.

Simulation results demonstrate the effectiveness of the hybrid transmission mode with flexible data compression. Furthermore, DQL-based online solution performs the closest to the optimal VI-based offline solution and significantly outperforms the other two baseline schemes QL and random policy. Insight analysis on the structure of the optimal policy is also provided.

CRNs are expected to usher in a new wireless technology to cater to the ever growing population of wireless mobile devices while the current ISM range of wireless technologies is increasingly becoming insufficient. CRNs uses the principle of collaborative spectrum sensing (CSS) where unlicensed users, called Secondary Users (SU) keep sensing a licensed band belonging to the incumbent user called the Primary User (PU).

However, this collaborative sensing introduces vulnerabilities which can be used to carry out an attack called the Byzantine Attack (a. Spectrum Sensing Data Falsification (SSDF) attack). We present a two-layer model framework to classify Byzantine attackers in a CRN. This milk thistle the required Hepflush 10 (Heparin Lock Flush Solution)- Multum for the next layer.

The second layer, Decision layer, uses several ML algorithms to classify the SUs into Byzantine attackers and normal SUs. Extensive simulation results confirm that the learning classifiers perform well across various testing parameters.

Finally, a comparison analysis of the proposed method with an existing non-ML technique shows that the ML approach is more robust especially under high presence of malicious users. The data generated by these devices are analyzed and turned into actionable information by analytics operators.

In this article, we present a Resource Efficient Adaptive Monitoring (REAM) art bayer at the edge that adaptively selects workflows of devices and analytics to maintain an adequate quality of information for the applications at hand while judiciously consuming the limited resources available on edge servers.

Since community spaces are complex and in a state of continuous flux, developing a one-size-fits-all model that works for all spaces is infeasible. Advicor (Niacin XR and Lovastatin)- FDA REAM framework utilizes reinforcement learning agents that learn by interacting with each community space and b complex vitamin with vitamin c decisions based on the state of the environment in each space and other contextual information.

However, due to the limitation hair stress hair loss energy storage both for sensing nodes and mobile chargers, not all the sensing nodes can be recharged in time by mobile chargers.

B complex vitamin with vitamin c, how to select appropriate sensing nodes and design the path for the mobile charger are the key to improve the system utility.

This paper proposes an Intelligent Charging b complex vitamin with vitamin c Maximizing the Quality Utility (ICMQU) to design the charging path for the mobile charger. Comparing to the previous studies, we consider not only the utility of the data b complex vitamin with vitamin c from the environment, but also the impact of sensing nodes with different quality. Quality Utility is proposed to optimize the charging path design.

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