MAC design for WiFi infrastructure networks: a game-theoretic approach

In WiFi networks, mobile nodes compete for accessing a shared channel by means of a random access protocol called Distributed Coordination Function (DCF). Although this protocol is in principle fair, since all the stations have the same probability to transmit on the channel, it has been shown that unfair behaviors may emerge in actual networking scenarios because of non-standard configurations of the nodes. Due to the proliferation of open source drivers and programmable cards, enabling an easy customization of the channel access policies, we propose a game-theoretic analysis of random access schemes. Assuming that each node is rational and implements a best response strategy, we show that efficient equilibria conditions can be reached when stations are interested in both uploading and downloading traffic. More interesting, these equilibria are reached when all the stations play the same strategy, thus guaranteeing a fair resource sharing. When stations are interested in upload traffic only, we also propose a mechanism design, based on an artificial dropping of layer-2 acknowledgments, to force desired equilibria. Finally, we propose and evaluate some simple DCF extensions for practically implementing our theoretical findings.Comment: under review on IEEE Transaction on wireless communication

Timely Data Delivery in a Realistic Bus Network

Abstract—WiFi-enabled buses and stops may form the backbone of a metropolitan delay tolerant network, that exploits nearby communications, temporary storage at stops, and predictable bus mobility to deliver non-real time information. This paper studies the problem of how to route data from its source to its destination in order to maximize the delivery probability by a given deadline. We assume to know the bus schedule, but we take into account that randomness, due to road traffic conditions or passengers boarding and alighting, affects bus mobility. We propose a simple stochastic model for bus arrivals at stops, supported by a study of real-life traces collected in a large urban network. A succinct graph representation of this model allows us to devise an optimal (under our model) single-copy routing algorithm and then extend it to cases where several copies of the same data are permitted. Through an extensive simulation study, we compare the optimal routing algorithm with three other approaches: minimizing the expected traversal time over our graph, minimizing the number of hops a packet can travel, and a recently-proposed heuristic based on bus frequencies. Our optimal algorithm outperforms all of them, but most of the times it essentially reduces to minimizing the expected traversal time. For values of deadlines close to the expected delivery time, the multi-copy extension requires only 10 copies to reach almost the performance of the costly flooding approach. I

Towards Inference Delivery Networks: Distributing Machine Learning with Optimality Guarantees

An increasing number of applications rely on complex inference tasks that are based on machine learning (ML). Currently, there are two options to run such tasks: either they are served directly by the end device (e.g., smartphones, IoT equipment, smart vehicles), or offloaded to a remote cloud. Both options may be unsatisfactory for many applications: local models may have inadequate accuracy, while the cloud may fail to meet delay constraints. In this paper, we present the novel idea of \emph{inference delivery networks} (IDNs), networks of computing nodes that coordinate to satisfy ML inference requests achieving the best trade-off between latency and accuracy. IDNs bridge the dichotomy between device and cloud execution by integrating inference delivery at the various tiers of the infrastructure continuum (access, edge, regional data center, cloud). We propose a distributed dynamic policy for ML model allocation in an IDN by which each node dynamically updates its local set of inference models based on requests observed during the recent past plus limited information exchange with its neighboring nodes. Our policy offers strong performance guarantees in an adversarial setting and shows improvements over greedy heuristics with similar complexity in realistic scenarios

Large Scale Control of Deferrable Domestic Loads in Smart Grids

In this paper, we investigate a realistic and low- cost deployment of large scale direct control of inelastic home appliances whose energy demand cannot be shaped, but simply deferred. The idea is to exploit 1) some simple actuators to be placed on the electric plugs for connecting or disconnecting appliances with heterogeneous control interfaces, including non- smart appliances, and 2) the Internet connections of customers for transporting the activation requests from the actuators to a centralized controller. Our solution requires no interaction with home users: in particular, it does not require them to express their energy demand in advance. A queuing theory model is derived to quantify how many users should adopt this solution in order to control a significant aggregated power load without significantly impairing their quality of service

Extracts from microalga chlorella sorokiniana exert an anti-proliferative effect and modulate cytokines in sheep peripheral blood mononuclear cells

The objective of this experiment was to study the effects of the unsaponified fraction (UP), the acetylated unsaponified fraction (AUP), and the total lipid fraction (TL) extracted and purified from Chlorella sorokiniana (CS) on the proliferation and cytokine profile of sheep peripheral blood mononuclear cells (PBMCs). Cells were cultured with 0.4 mg/mL and 0.8 mg/mL concentrations of each extract (UP, AUP, and TL fractions) and activated with 5 μg/mL concanavalin A (ConA) and 1 μg/mL lipopolysaccharide (LPS) at 37 °C for 24 h. PBMCs cultured with ConA and LPS represented the stimulated cells (SC), and PBMCs without ConA and LPS represented the unstimulated cells (USC). Cell-free supernatants were collected to determine IL-10, IL-1β, and IL-6 secretions; on cells, measurement of proliferation was performed. All the extracts tested significantly decreased the cell proliferation; in particular, the UP fraction at 0.4 mg/mL showed the lowest proliferative response. Furthermore, at 0.8 mg/mL, the UP fraction enhanced IL-10 secretion. On the contrary, the TL fraction at 0.4 mg/mL induced an increase in IL-10, IL-6, and, to a lesser extent, IL-1β secretions by cells. The AUP fraction did not change cytokine secretion. The results demonstrated that CS extracts could be useful ingredients in animal feed in order to minimize the use of antibiotics by modulating cell proliferation and cytokine response

Follicular dynamics in synchronized Italian Mediterranean buffalo cows

The aim of this study was to evaluate the length and the characteristics of the oestrous cycle in Italian Mediterranean buffalo cows, undergone synchronization of ovulation. The trial was performed on 32 buffaloes synchronized by the Ovsynch Program, which consists of an injection of GnRH on day 0, PGF2α on day 7 and GnRH on day 9. Starting on day 10 (Day 0 of the new cycle). Buffaloes undergone ultrasound examination of the ovaries on alternate days until the following heat. Follicular growth and corpus luteum formation and dimensions were recorded as well as the number of follicular waves. Statistical analysis was performed by ANOVA. Four animals (12.5%) did not show signs of oestrous and were excluded from the trial. The mean length of the oestrous cycle was 23.7±3.4 days. In particular, 1 animal (3.6%) showed an oestrous cycle characterized by 1 follicular wave with a length of 16 days, 17 subjects (60.7%) showed 2 follicular waves with a cycle length of 22.4±2.3 days and 10 buffaloes (35.7%) showed 3 follicular waves with a cycle of 26.8±2.0 days. These results confirm previous reports performed in buffalo species, although the cycle resulted longer in the 3-waves group

A preliminary study on metabolome profiles of buffalo milk and corresponding mozzarella cheese: Safeguarding the authenticity and traceability of protected status buffalo dairy products

The aim of this study is to combine advanced GC-MS and metabolite identification in a robust and repeatable technology platform to characterize the metabolome of buffalo milk and mozzarella cheese. The study utilized eleven dairies located in a protected designation of origin (PDO) region and nine dairies located in non-PDO region in Italy. Samples of raw milk (100 mL) and mozzarella cheese (100 g) were obtained from each dairy. A total of 185 metabolites were consistently detected in both milk and mozzarella cheese. The PLS-DA score plots clearly differentiated PDO and non-PDO milk and mozzarella samples. For milk samples, it was possible to divide metabolites into two classes according to region: those with lower concentrations in PDO samples (galactopyranoside, hydroxybuthyric acid, allose, citric acid) and those with lower concentrations in non-PDO samples (talopyranose, pantothenic acid, mannobiose, etc.,). The same was observed for mozzarella samples with the proportion of some metabolites (talopyranose, 2, 3-dihydroxypropyl icosanoate, etc.,) higher in PDO samples while others (tagatose, lactic acid dimer, ribitol, etc.,) higher in non-PDO samples. The findings establish the utility of GC-MS together with mass spectral libraries as a powerful technology platform to determine the authenticity, and create market protection, for “Mozzarella di Bufala Campana.