Time constants in spiking neural networks (SNNs) are crucial for determining performance. While prior work shows that learning time constants can improve accuracy, it typically assumes near-optimal initial values and rarely examines recovery from poor initializations. We systematically study how membrane and synaptic time constants affect SNN performance using multiple training strategies. Our results show that suboptimal values for time constants can reduce accuracy by nearly 10%, but networks can recover through optimization during the training process.

User generated content (UGC) makes up a significant portion of Internet traffic. As opposed to other content, UGC has so far been left outside over-the-top providing network operators content distribution networks (telco-CDN) due to the difficulty in determining optimised placement of such content. The side effect of this is that UGC content is not placed close to end users and therefore occupy unnecessary network resources. The difficulty in determining optimal placement of UGC stems from the different geographical and dynamic behaviour of the content generators, and a further complication is that with UGC, it is necessary to place content in real-time which this has an impact on performance optimality. Even though CDNs have been widely studied in the literature, little attention has been given to the challenging case of UGC placement. In this paper, we propose an on-line placement algorithm and compare its performance with the off-line counterpart based on integer programming, both under the assumption that the popularity of content is known to the algorithms. In order to determine the popularity, we present an on-line learning model to predict spatial patterns in content requests. Furthermore, we couple the model with an algorithm for learning the early popularity of content, i.e., shortly after the content becomes known. We show that together, these approaches enable service providers to effectively place UGC and minimise the cost of serving UGC in their networks.

Given the complexity and heterogeneity in Cloud computing scenarios, the modeling approach has widely been employed to investigate and analyze the energy consumption of Cloud applications, by abstracting real-world objects and processes that are difficult to observe or understand directly. It is clear that the abstraction sacrifices, and usually does not need, the complete reflection of the reality to be modeled. Consequently, current energy consumption models vary in terms of purposes, assumptions, application characteristics and environmental conditions, with possible overlaps between different research works. Therefore, it would be necessary and valuable to reveal the state-of-the-art of the existing modeling efforts, so as to weave different models together to facilitate comprehending and further investigating application energy consumption in the Cloud domain. By systematically selecting, assessing, and synthesizing 76 relevant studies, we rationalized and organized over 30 energy consumption models with unified notations. To help investigate the existing models and facilitate future modeling work, we deconstructed the runtime execution and deployment environment of Cloud applications, and identified 18 environmental factors and 12 workload factors that would be influential on the energy consumption. In particular, there are complicated trade-offs and even debates when dealing with the combinational impacts of multiple factors.

Cooperative content replication and request routing (C2R3) has emerged as a promising technique to enhance the efficiency of content delivery networks (CDN). Most existing approaches to C2R3focus on efficient bandwidth usage and assume a hierarchical CDN architecture targeted towards the delivery of specific content types (e.g., video). Therefore, C2R3problem of covering the broad range of content types with minimum content access delay in a general CDN architecture has attracted little attention. As a potential solution to C2R3, cooperative web caching techniques have become mature. However, these techniques were designed to improve performance indicators tailored to web contents only (i.e., hit rate and byte hit rate). Arguably, improving such indicators does not necessarily lead to optimal access delay especially when the current trend of user-generated contents with diverse popularities and sizes are taken into account. In this paper, we formulate C2R3as an optimization problem with the objective of minimizing content access delay in a general CDN architecture. A new performance indicator is introduced, and two popularity-based cooperative algorithms are proposed to approach the NP-hard C2R3problem. Under broad ranges of cache size and popularity distribution parameters, we compare the proposed methods with a cooperative recency-based web caching method. Our simulation results show that the popularity-based methods outperform the recency-based method, and demonstrate close to optimal performance in representative scenarios of real-world situations.

Intelligent transport information systems (TIS) are one of the most rapidly emerging applications of wireless vehicular communications using DSRC/WAVE technology. A core function in a TIS is the estimation of traffic density using beacons transmitted by individual vehicles, subsequently used for purposes such as traffic light control or incident detection. In this paper, we conduct an in-depth study on how the accuracy of traffic density estimation depends on various parameters of the wireless beacons transmission policy, such as beaconing rate and timing. Unlike many other studies, our focus is entirely on the application-layer metric of density estimation accuracy, rather than low-layer metrics such as packet loss rate and throughput. Our results are evaluated over a wide range of traffic traces generated by a commercial microscopic traffic simulator (Paramics), corresponding to a typical urban scenario consisting of a signalized intersection of multiple-lane roads.

In the emerging Delay Tolerant Networks (DTNs) content dissemination platform, mobile nodes opportunistically exchange content as they meet, with the intent of sharing content among nodes with common interests. During a meeting, nodes can exchange both content of direct interest to themselves as well as content that is of interest to a larger set of nodes that may be encountered in the future. The utility of a DTN is governed by the content exchange opportunity (the amount of content that can be exchanged during a meeting) as well as the selection of content to be exchanged in order to maximise the interest nodes will have in information they are exposed to. Considering that there is a cost associated with the content exchange (e.g. battery usage, buffer occupancy or consumed transmission opportunity) the aim for nodes participating in content dissemination should be to maximise their payoff. In this paper, we contribute a generic framework for describing the characteristics of content exchange among participating nodes in a network. We incorporate a distributed information popularity measurement and the pairwise interaction of nodes modelled as a bargaining problem. The outcome of this process is the fair split up of transmission opportunity as a network resource and the selection of content objects to exchange in order to maximise the nodes' payoff. The framework is generally intended as a capstone for investigation of content dissemination properties and various content exchange strategies in a DTN. The paper further presents experiments conducted to validate the function and correctness of the proposed framework.

Vehicular traffic density is a key factor in determining the behaviour of radio overlapping and thus impacts performance of data and safety message communication in Vehicular Ad hoc Networks (VANETs). In this paper, we propose a novel density model for urban traffic systems and employ this model for the purpose of spatial-temporal analysis of radio overlapping. To model traffic density, we consider a signalized junction and road segments linked to that junction as basic building blocks of urban traffic systems. The density model enables us to derive a framework to explain trends and critical regions of radio overlapping corresponding to VANET scenarios targeted for urban transportation systems, which cannot be derived from uniform density models widely used in existing literature. We apply the derived radio overlapping model to study channel load associated with periodic beaconing, a fundamental mechanism for safety message communication in VANETs. This study also provides a generic analytical framework to investigate other performance aspects of data and safety message communication in VANETs. Our experimental results using the proposed realistic density model demonstrate the need for an adaptive mechanism to adjust transmission power and data rate to reduce channel loads associated with dense traffic conditions.

Road safety is one of the most important emerging applications envisioned for Vehicular Ad hoc Networks (VANETs). Generally, such applications involve the broadcast of safety messages, consisting of beacons transmitting vehicles' state (e.g. position and velocity) with a regular period, as well as emergency messages warning about unexpected critical events. From the perspective of safety, the application performance depends foremost on two metrics: for the event-driven warning messages, the probability of message reception; and for periodic messages, the variability of the inter-reception time (IRT), which ultimately determines the freshness of the information received by the driver. In this paper, we develop an analytical model to compute the above metrics in an urban traffic scenario. Focusing on a road segment linked to a signalized junction as a basic building block of urban traffic systems, we apply a novel road traffic density model to investigate the dynamics of the reliability metrics and characterize the region(s) on the road segment according to the achieved safety level. Our numerical study shows that in broadcast mode, the hidden terminal effect is the driving factor determining the reliability of transmissions. Furthermore, the impact of hidden terminals has the greatest effect in road sections where vehicles have high velocity, leading to the poorest performance in regions where reliable reception is needed the most in order to minimize the risk of accidents.