We study the existence of infinite and safe schedules for resource-dependent real-time systems, in the setting of multiple continuous resources. Specifically, we explore the multi-variable extension of Energy Timed Automata, where variables are bounded by polyhedra in . We ask the question of whether there exist infinite runs satisfying such boundary constraints and show how schedules can be synthesized by characterising these runs as limit sets using quantifier elimination for linear real arithmetic. We show that for linear limit sets, it is possible to characterise such infinite runs. Additionally, we relate this to an earlier decidability result for single-variable Energy Timed Automata that are flat and segmented, and show constructively that there exist flat and segmented multi-variable Energy Timed Automata that give rise to non-linear limit sets. Lastly, we solidify our framework and method with a case study. Specifically, a multi-agent extension of an industrial case concerned with oil tanks, originally provided by the HYDAC company.

We study the problem of verifying schedulability and ascertaining response time bounds of limited-preemptive parallel applications with uncertainty, scheduled on multi-core platforms. While sufficient techniques exist for analysing schedulability and response time of parallel applications under fixed-priority scheduling, their accuracy remains uncertain due to the lack of a scalable and exact analysis that can serve as a ground-truth to measure the pessimism of existing sufficient analyses. In this paper, we address this gap using formal methods. We use Timed Automata and the powerful UPPAAL verification engine to develop a generic approach to model parallel applications and provide a scalable and exact schedulability and response time analysis. This work establishes a benchmark for evaluating the accuracy of both existing and future sufficient analysis techniques. Furthermore, our solution is easily extendable to more complex task models thanks to its flexible model architecture.

In a cyber-physical system (CPS), different entities often interact with each other across time. With the development of various sensing technologies, the time-varying interactions among entities are often recorded as multiple, correlated time series. A typical CPS is a road transportation system, where the traffic on different road segments interact with each other. Traffic sensors are often deployed to capture travel speeds on different road segments, which results in multiple, potentially correlated, speed time series. Under this setting, an increasingly pertinent task is to forecast future speeds, which is essential in a wide variety of traffic planning scenarios. We present a system for correlated time series forecast. The system is able to employ different learning algorithms to perform correlated time series forecast, which facilities end users to choose the most appropriate algorithm for their specific service. The system is developed and integrated into aSTEP, a spatio-temporal data analytic platform developed by Aalborg University, and is tested using a wide variety of correlated time series data, including a user demand time series from a local mobility-as-a-service company.

Real-time streaming of audio and video have tight constraints on the delay allowed at each step in the distribution chain. In this paper, we focus on the transport layer. That is, we investigate the effect of imposing a maximum allowable delay on the symbol loss probability for a set of rate 1/2 erasure correcting codes. We define the effective symbol loss probability to be the probability that a symbol is received too late or not at all, given some maximum allowable delay. We consider networks where the source and sink communicate via a relaying node, and that this intermediate node performs recoding. The erasure correcting code used between the source and relay need not be the same as the erasure correcting code used between the relay and sink. Moreover, on the first link, we use both a block code and on the second link we use a convolutional code. In order to do a fair comparison, we also include the case where both links use the same code type. Our results show that in order to minimize the effective symbol loss, the first link should use a triangular block code. Whereas, the second link should use a convolutional or triangular code, with little to no gain of using the former over the latter.

In this paper, we present explicit code constructions for a family of (n,k,δ) convolutional codes with optimum distance profiles. The family of convolutional codes is obtained from sets of jointly superregular matrices. For the case of finite decoder memory, we evaluate the performance of the constructed codes in terms of both symbol loss probability and symbol delay. We then present a combinatorial method to calculate the exact symbol loss probability and symbol delay for each symbol individually. We compare the symbol loss probability for two specific systematic convolutional codes for the cases where the sink has infinite or finite memory. Finally, we compare the performance of our convolutional codes with optimum distance profile and random based convolutional codes.

A matrix is termed superregular if all of its possible submatrices are non-singular. Superregular lower triangular Toeplitz matrices are useful for MDS convolutional codes and (sequential) network codes. In this paper, we present the explicit matrix constructions for superregular lower triangular Toeplitz matrices in GF(2p)k×k, k ≤ 5. For k > 5 we provide a greedy algorithm, which (over sufficiently large fields) is guaranteed to find a superregular lower triangular Toeplitz matrix. We introduce (product preserving) joint superregularity, and extend our explicit matrix constructions to these cases. We provide methods for deriving the exact symbol loss probability and delay for any deterministic block code. We derive the exact symbol loss probability and delay for codes using a superregular lower triangular matrix and for codes using two (product preserving) jointly superregular lower triangular matrices. We then compare these results with those obtained from both simulations and our practical implementation, and for each case we also compare with random-based codes. Furthermore, our experiments show a gain in coding throughput above 40% for superregular lower triangular Toeplitz matrices over random matrices.

Superregular matrices have the property that all of their submatrices, which can be full rank are so. Lower triangular superregular matrices are useful for e.g., maximum distance separable convolutional codes as well as for (sequential) network codes. In this work, we provide an explicit design for all superregular lower triangular Toeplitz matrices in GF(2p) for the case of matrices with dimensions less than or equal to 5 × 5. For higher dimensional matrices, we present a greedy algorithm that finds a solution provided the field size is sufficiently high. We also introduce the notions of jointly superregular and product preserving jointly superregular matrices, and extend our explicit constructions of superregular matrices to these cases. Jointly superregular matrices are necessary to achieve optimal decoding capabilities for the case of codes with a rate lower than 1/2, and the product preserving property is necessary for optimal decoding capabilities in network recoding.

Packet losses in wireless networks dramatically curbs the performance of TCP. This paper introduces a simple coding shim that aids IP-layer traffic in lossy environments while being transparent to transport layer protocols. The proposed coding approach enables erasure correction while being oblivious to the congestion control algorithms of the utilised transport layer protocol. Although our coding shim is indifferent towards the transport layer protocol, we focus on the performance of TCP when ran on top of our proposed coding mechanism due to its widespread use. The coding shim provides gains in throughput that exceed 10x for TCP traffic while requiring a limited sacrifice in terms of fairness towards other flows on the channel.

State-of-the-art in network coding for wireless, meshed networks typically considers two problems separately. First, the problem of providing reliability for a single session. Second, the problem of opportunistic combination of flows by using minimalistic coding, i.e., by XORing packets from different flows. Instead of maintaining these approaches separate, we propose a protocol (CORE) that brings together these coding mechanisms. Our protocol uses random linear network coding (RLNC) for intra- session coding but allows nodes in the network to setup inter- session coding regions where flows intersect. Routes for unicast sessions are agnostic to other sessions and setup beforehand, CORE will then discover and exploit intersecting routes. Our approach allows the inter-session regions to leverage RLNC to compensate for losses or failures in the overhearing or transmitting process. Thus, we increase the benefits of XORing by exploiting the underlying RLNC structure of individual flows. This goes beyond providing additional reliability to each individual session and beyond exploiting coding opportunistically. Our numerical results show that CORE outperforms both forwarding and COPE-like schemes in general. More importantly, we show gains of up to 4 fold over COPE-like schemes in terms of transmissions per packet in one of the investigated topologies.