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Traffic congestion in urban areas poses several challenges to municipal authorities including pollution, productivity loss, reckless driving, and delays in dealing with emergencies. Smart cities can use modern IoT infrastructure to solve the congestion problem and reduce pollution and delays. In this article, we focus on congestion mapping and mitigation for emergency vehicles in smart cities. We use a novel traffic light control technique to change the flow of cars on lights of interest thereby making way for emergency vehicles. We use a simulation model for a selected area of Manhattan to implement congestion mapping and to help find the fastest path for routing emergency vehicles based on the congestion metrics. The system controls traffic lights to block off the roads feeding into congestion and allows flow away from the congested path. This helps in clearing the preferred route to help emergency vehicles reach the destination faster. We show that the proposed algorithm can map congestion on city roads with accuracy thus helping to improve the response time of the emergency services and saving precious lives. [ABSTRACT FROM AUTHOR]

The IEEE 802.15.4 standard is known as the most used technology for Wireless Sensor Networks (WSNs) because of their low rate and especially their energy efficiency. To better support the requirements of Low Energy Critical Infrastructure Monitoring (LECIM) applications, the IEEE 802.15.4k is approved. This amendment is intended for critical packets transmission using the Priority Channel Access (PCA) that has become a real issue in infrastructures applications. The analysis presented in this paper investigates, for the first time in the literature, the performance analysis of the IEEE 802.15.4k in non beacon-enabled mode assuming star topology, saturated traffic and fading channel conditions. Where fading channel is a very important factor in the network as it affects its performance. We emphasize that this is an original work that has never been treated in literature. In this paper, we have developed a two dimensional Markov chain model for the IEEE 802.15.4k unslotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) with PCA backoff mechanism, and, a three dimensional Markov chain model for the IEEE 802.15.4k unslotted CSMA/CA mechanism. Furthermore, to test the efficiency of the proposed model, we have analyzed the impact of variation of network size, probability that the available packet is critical, Bit Error Rate (BER), and packet length on the reliability, power consumption and throughput in order to compare the performance of both mechanisms. With the increase of network size and packet length, it is observed that PCA achieves a reduction in energy, a higher throughput and a lower reliability compared to CSMA/CA. The obtained results are satisfactory because packet length and network size are the most important parameters in industrial applications especially in critical systems. Finally, a comparative table was given to compare the behavior of the IEEE 802.15.4k network in terms of performance metrics. [ABSTRACT FROM AUTHOR]

Resource management is the process of task scheduling and resource provisioning to provide requirements of cloud users. Since cloud resources are often heterogeneous, task scheduling and resource provisioning are major challenges in this area. Various methods have been introduced to improve resource utilization and thus increase the efficiency of cloud computing. Existing methods can be divided into several categories, including mathematical and statistical methods, heuristic- and meta-heuristic-based methods, and machine-learning-based methods. Since the resource management problem is NP-complete, several optimization methods have been also exploited in this area. Coral reefs algorithm is an evolutionary method that has showed appropriate convergence and response time for some problems, and thus is used in this paper to combine with reinforcement learning to improve efficiency of resource management in cloud environments. The proposed method of this paper consists of two phases. The initial allocation of resources to ready-to-perform tasks is done using the coral reefs algorithm in the first phase. The tasks are considered as corals and the resources are considered reefs in this method. The second phase utilizes reinforcement learning to avoid falling into the local optima and to make optimal use of resources using a long-term approach. The proposed model of this paper, called MO-CRAML, introduces a new hybrid algorithm for improving utilization and load balancing of cloud resources using the combination of coral reefs optimization algorithm and reinforcement learning. The results of the experiments show that the proposed algorithm has better performance in cloud resource utilization and load balancing in comparison with some other important methods of the literature. [ABSTRACT FROM AUTHOR]

Transmission delay, throughput and energy are important criterions to consider in wireless sensor networks (WSN). In this way, IEEE 802.15.4 standard was conceived with the objective to reduce resource’s consumption in both WSN and wireless personal area networks. In such networks, the slotted CSMA/CA still occupies a prominent place as a channel control access mechanism with its inherent simplicity and reduced complexity. In this paper, we propose to introduce a network allocation vector (NAV), to reduce energy consumption and collisions in IEEE 802.15.4 networks. A Markov chain-based analytical model of the fragmentation mechanism, in a saturated traffic, is given as well as a model of the energy consumption using the NAV mechanism. The obtained results show that the fragmentation technique improves at the same time the throughput, the access delay and the bandwidth occupation. They, also, show that using the NAV allows reducing significantly the energy consumption when applying the fragmentation technique in slotted CSMA/CA under saturated traffic conditions. [ABSTRACT FROM AUTHOR]

High availability in cloud computing services is essential for maintaining customer confidence and avoiding revenue losses due to SLA violation penalties. Since the software and hardware components of cloud infrastructures may have limited reliability, the use of redundant components and multiple clusters may be required to achieve the expected level of dependability while also increasing the computational capacity. A drawback of such improvements is the respective impact on the capital and increase in acquisition and operational costs. This paper presents availability models for private cloud architectures based on Eucalyptus platform, and presents a comparison of costs between these architectures and similar infrastructure rented from a public cloud provider. Metrics for capacity-oriented availability and system steady-state availability are used to compare architectures with distinct numbers of clusters. A heterogeneous hierarchical modeling approach is employed to represent the systems considering both hardware and software failures. The results highlight that improvements on the availability are not significant when increasing the system to more than two clusters. The analysis also shows that the average available capacity is close to the maximum possible capacity in all architectures, and that it takes 18 months, in average, for these private cloud architectures to pay off the cost equivalent to the computational capacity rented from a public cloud. [ABSTRACT FROM AUTHOR]