Modern society is facing great challenges due to pollution and increased carbon dioxide (CO2) emissions. As part of solving these challenges, the use of renewable energy sources and electric vehicles (EVs) is rapidly increasing. However, increased dynamics have triggered problems in balancing energy supply and consumption demand in the power systems. The resulting uncertainty and unpredictability of energy production, consumption, and management of peak loads has caused an increase in costs for energy market actors. Therefore, the means for studying the balancing of local smart grids with EVs is a starting point for this paper. The main contribution is a simulation-based approach which was developed to enable the study of the balancing of local distribution grids with EV batteries in a cost-efficient manner. The simulation-based approach is applied to enable the execution of a distributed system with the simulation of a local distribution grid, including a number of charging stations and EVs. A simulation system has been constructed to support the simulation-based approach. The evaluation has been carried out by executing the scenario related to balancing local distribution grids with EV batteries in a step-by-step manner. The evaluation results indicate that the simulation-based approach is able to facilitate the evaluation of smart grid-and EV-related communication protocols, control algorithms for charging, and functionalities of local distribution grids as part of a complex, critical OPEN ACCESSSystems 2015, 3 82 cyber-physical system. In addition, the simulation system is able to incorporate advanced methods for monitoring, controlling, tracking, and modeling behavior. The simulation model of the local distribution grid can be executed with the smart control of charging and discharging powers of the EVs according to the load situation in the local distribution grid. The resulting simulation system can be applied to the study of balancing local smart grids with EV batteries. Based on the evaluation results, it is estimated that the simulation-based approach can provide an essential, safe, and cost-efficient method for the evaluation of complex, critical cyber-physical systems, such as smart grids.

S-NET is a declarative coordination language and component technology primarily aimed at modern multicore/many-core chip architectures. It builds on the concept of stream processing to structure dynamically evolving networks of communicating asynchronous components. Components themselves are implemented using a conventional language suitable for the application domain.We present the design and implementation of Distributed S-NET, a conservative extension of S-NET geared towards distributed memory architectures ranging from many-core chip architectures with hierarchical memory organizations to more traditional clusters of workstations, supercomputers and grids.Three case studies illustrate how to use Distributed S-NET to implement different models of parallel execution. Runtimes obtained on a workstation cluster demonstrate how Distributed S-NET allows programmers with little or no background in parallel programming to make effective use of distributed memory architectures with minimal programming effort.

The forthcoming communication networks for public safety authorities rely on the fifth generation (5G) of mobile networking technologies. Police officers, paramedics, border guards, as well as fire and rescue personnel, will connect through commercial operator's access network and rapidly deployable tactical bubbles. This transition from closed and dedicated infrastructure to hybrid architecture will expand the threat surface and expose mission-critical applications and sensitive information to cyber and physical adversaries. We explore and survey security architecture and enablers for prioritized public safety communication in 5G networks. We identify security threat scenarios and analyze enabling vulnerabilities, threat actors, attacks vectors, as well as risk levels. Security enablers are surveyed for tactical access and core networks, commercial infrastructure, and mission-critical applications, starting from push-to-talk and group video communication and leading to situational-awareness and remote-controlled systems. Two solutions are trialed and described in more detail: remote attestation enhanced access control for constrained devices, and securing of satellite backhauls. We also discuss future research directions highlighting the need for enablers to automate security of rapid deployments, for military-grade cost-effective customizations of commercial network services to ensure robustness, and for hardening of various types of public safety equipment.

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Copyright ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 5th ACM SIGPLAN Workshop on Declarative Aspects of Multicore Programming: http://doi.acm.org/10.1145/1708046.1708054S-Net is a declarative coordination language and component technology aimed at modern multi-core/many-core architectures and systems-on-chip. It builds on the concept of stream processing to structure dynamically evolving networks of communicating asynchronous components. Components themselves are implemented using a conventional language suitable for the application domain. This two-level software architecture maintains a familiar sequential development environment for large parts of an application and offers a high-level declarative approach to component coordination. In this paper we present a conservative language extension for the placement of components and component networks in a multi-memory environment, i.e. architectures that associate individual compute cores or groups thereof with private memories. We describe a novel distributed runtime system layer that complements our existing multithreaded runtime system for shared memory multicores. Particular emphasis is put on efficient management of data communication. Last not least, we present preliminary experimental data

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