The electricity production and distribution is facing two major changes. First, the production is shifting from classical energy sources such as coal and nuclear power towards renewable resources such as solar and wind. Secondly, the consumption in the low voltage grid is expected to grow significantly due to expected introduction of electrical vehicles. The first step towards more efficient operational capabilities is to introduce an observability of the distribution system and allow for leveraging the flexibility of end connection points with manageable consumption, generation and storage capabilities. Thanks to the advanced measurement devices, management framework, and secure communication infrastructure developed in the FP7 SUNSEED project, the Distribution System Operator (DSO) now has full observability of the energy flows at the medium/low voltage grid. Furthermore, the prosumers are able to participate pro-actively and coordinate with the DSO and other stakeholders in the grid. The monitoring and management functionalities have strong requirements to the communication latency, reliability and security. This paper presents novel solutions and analyses of these aspects for the SUNSEED scenario, where the smart grid ICT solutions are provided through shared cellular LTE networks.

This paper presents a novel, practical, routing-independent networkcoding algorithm: BON-Bearing opportunistic network coding. Simplicity is its main benefit as it introduces little overhead to the network since nodes do not need to keep track of received traffic for their neighbouring nodes. Algorithm makes coding decisions based solely on the information about the packet previous and next hop node position. Algorithm functions between the MAC and link layers, with small modifications made only to the MAC layer. Using different topologies and different traffic loads and distributions in the simulation model we evaluated algorithm performance and compared it to a well-known COPE algorithm.

tudies on Internet traffic trends show staggering growth rates of between 70 and 115 percent per year [1]. This remarkable expansion in growth is the result of new applications, modifications in user habits, and changing trends: • New industries and operator types such as data-center providers and cloud operators are present in the market. • Devices that enable "non-stop" content generation and consumption, such as tablet computers and smartphones, have found their way into our everyday lives. • New technologies have enabled a rapid change in user behavior patterns (e.g., instead of watching linear broadcast content via classic television, the same or personalized and self-selected content can be accessed via YouTube). • DVD stores or rental outlets seem to be forgotten as video content can be accessed through online streaming services such as Netflix. At the moment mobile Internet traffic represents 15 percent of total Internet traffic. However, the trends in 2011 and 2012 suggest that mobile Internet traffic is increasing at 50 percent per year, and that tablet computer users generate 2.5 times more traffic than those using smartphones. In addition, the migration to the fourth-generation (4G) mobile communications technology standards such as Long Term Evolution (LTE) promise 19 times more traffic than we had to cope with in the 3G mobile communications technology standards. By 2017 this will result in 70 percent of traffic having its origin in mobile devices, with mobile traffic increasing by 13 times and that of video by 16 times. For these reasons, telecoms are being pushed into the development of systematic methodologies and tools that enable them to cope in a timely and costeffective way with such abrupt changes in network traffic demands [2].Efficient network design and capacity dimensioning have to be employed to provide network resources that meet the traffic demands. However, a balance between investments maintaining network-level quality of service (QoS) and providing end-user satisfaction (i.e., a good quality of experience, QoE) even in partial network failure situations has to be found in a competitive business environment. Since service providers are introducing new services and billing schemes, which can heavily influence the traffic load on a regular basis, it is of paramount importance that such changes are analyzed appropriately in a simulation environment in order to omit network and server congestion, or even fallouts, and maintain the desired QoE.The modeling of network traffic [3] for load prediction is a necessary tool for modern telecom operators. It allows them to close the life cycle control loop as follows: network development -deployment -operations -optimizationupgrade of expensive equipment. Simulation models allow the close monitoring of planned network upgrades and their performance evaluation. "What-if" simulation scenarios are especially appealing for planned (not yet deployed) network elements and services, since we can predict the behavior of the entire network by taking into...

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