43 research outputs found
Realistic Analysis of Limited Parallel Software / Hardware Implementations
No full textProposed real-time system implementations combine reconfigurable hardware (for speed-up) with processor-memory architectures. Such hardware can execute many functions in parallel, leading to a limited parallel system where a single software process can execute on the processor at any time, in parallel with a number of functions implemented on the reconfigurable hardware. This approach is not amenable to conventional fixed priority timing analysis, as fundamental assumptions are compromised, namely that of a critical instant. This paper describes generalised fixed priority timing analysis for limited parallel systems, illustrated by an example system utilising field programmable gate arrays as the reconfigurable hardware resource
Mixed Criticality Scheduling of Probabilistic Real-Time Systems
Get PDFIn this paper we approach the problem of Mixed Criticality (MC) for probabilistic real-time systems where tasks execution times are described with probabilistic distributions. In our analysis, the task enters high criticality mode if its response time exceeds a certain threshold, which is a slight deviation from a more classical approach in MC. We do this to obtain an application oriented MC system in which criticality mode changes depend on actual scheduled execution. This is in contrast to classical approaches which use task execution time to make criticality mode decisions, because execution time is not affected by scheduling while the response time is. We use a graph-based approach to seek for an optimal MC schedule by exploring every possible MC schedule the task set can have. The schedule we obtain minimizes the probability of the system entering high criticality mode. In turn, this aims at maximizing the resource efficiency by the means of scheduling without compromising the execution of the high criticality tasks and minimizing the loss of lower criticality functionality. The proposed approach is applied to test cases for validation purposes
Design and implementation of ambiently powered Internet of Things-That-Think with asynchronous inference
No full textThis work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) through the “First Call for H.F.R.I. Research Projects to support Faculty Members and Researchers and the Procurement of High-Cost Research Equipment” under Project 2846.
This article was presented in part at 5th IEEE International Workshop on Wireless Communications and Networking in Extreme Environments (WCNEE), International Conference on Distributed Computing in Sensor Systems (DCOSS), Pafos, Cyprus, July 2021, pp. 458–465 [DOI: 10.1109/DCOSS52077.2021.00077]. (Vasileios Papageorgiou, Athanasios Nichoritis, and Panagiotis Vasilakopoulos contributed equally to this work.) (Corresponding author: Aggelos Bletsas.)Summarization: This work offers design and implementation of in-network inference, using message passing among ambiently powered wireless sensor network (WSN) terminals. The stochastic nature of ambient energy harvesting dictates intermittent operation of each WSN terminal and as such, the message passing inference algorithms should be robust to asynchronous operation. It is shown, perhaps for the first time in the literature (to the best of our knowledge), a proof of concept, where a WSN harvests energy from the environment and processes itself the collected information in a distributed manner, by converting the (network) inference task to a probabilistic, in-network message passing problem, often at the expense of increased total delay. Examples from Gaussian belief propagation and average consensus (AC) are provided, along with the derivation of a statistical convergence metric for the latter case. A k-means method is offered that maps the elements of the calculated vector to the different WSN terminals and overall execution delay (in number of iterations) is quantified. Interestingly, it is shown that there are divergent instances of the in-network message passing algorithms that become convergent, under asynchronous operation. Ambient solar energy harvesting availability is also studied, controlling the probability of successful (or not) message passing. Hopefully, this work will spark further interest for asynchronous message passing algorithms and technologies that enable in-network inference, toward ambiently powered, batteryless Internet of Things-That-Think.Presented on: IEEE Internet of Things Journa
Ομαδοποίηση αλγορίθμων συμπερασμού σε δίκτυα επικοινωνιών
No full textSummarization: This work offers an algorithmic framework for in-network inference, using message passing among ambiently powered wireless sensor network (WSN) terminals. The stochastic nature of ambient energy harvesting dictates intermittent operation of each WSN terminal and as such, the message passing inference algorithms should be robust to asynchronous operation. A version of Gaussian Belief Algorithm (GBP) is described, which can be reduced to an affine fixed point (AFP) problem, used to solve linear systems of equations. To achieve this, we have to cluster the Probabilistic Graphical Model (PGM) behind GBP, in order to map it to the WSN terminals. We propose two different clustering approaches, namely edge and node clustering. For the first approach, we explain the reasons why a previous method does not produce the expected results and we offer another method, which performs better. We also explain limitations of edge-based clustering. On the other hand, node clustering has a clear metric for performance, which is relevant to the number of edges connecting the different clusters. For this approach, we utilize three different clustering algorithms, the k-means, the spectral clustering and an autonomous, in-network clustering algorithm. Furthermore, we show in both theory and simulation that there is strong connection between spectral radius and the convergence rate of AFP problems with probabilistic asynchronous scheduling. The latter corroborates known theory for synchronous scheduling. Interestingly, it is shown through simulations that different clustering offers similar convergence rate, when probabilistic asynchronous scheduling is utilized with carefully selected probabilities that accelerate convergence rate in the mean sense. Finally, we show an existing distinction between convergence rate and energy consumption of the network and we present experimental results comparing the different clustering methods. In most cases, spectral clustering outperforms the rest, with reduced energy consumption (by a factor of 2 compared to k-means in specific cases)
Distributed beamforming and power allocation for cooperative networks.
Get PDFCooperative diversity systems rely on using relay nodes to relay copies of transmitted information to the destination such that each copy experiences different channel fading, hence increasing the diversity of the system. However, without proper processing of the message at the relays, the performance of the cooperative system may not necessarily perform better than direct transmission systems. In this paper, we proposed a distributed beamforming and power allocation algorithm which substantially improves the diversity of the system with only very limited feedback from the destination node. We also derive outage probability as well as study the outage behavior of this scheme
On-Demand Cooperation MAC Protocols with Optimal Diversity-Multiplexing Tradeoff
Get PDFThis paper presents access protocols with optimal Diversity-Multiplexing Tradeoff (DMT) performance in the context of IEEE 802.11-based mesh networks. The protocols are characterized by two main features: on-demand cooperation and selection of the best relay terminal. The on-demand characteristic refers to the ability of a destination terminal to ask for cooperation when it fails in decoding the message transmitted by a source terminal. This approach allows maximization of the spatial multiplexing gain. The selection of the best relay terminal allows maximization of the diversity order. Hence, the optimal DMT curve is achieved with these protocols
