A Delayed-ACK Scheme for Performance Enhancement of Wireless LANs

The IEEE 802.11 MAC protocol provides a reliable link layer using Stop & Wait ARQ. The cost for high reliability is the overhead due to acknowledgement packets in the direction opposite to the actual data flow. In this paper, the design of a new protocol as an enhancement of IEEE 802.11 is proposed, with the aim of reducing supplementary traffic overhead in order to increase the bandwidth available for actual data transmission. The performance of the proposed protocol is evaluated through comparison with IEEE 802.11 as well as with a SSCOP-based protocol. Results underline significant advantages of the proposed protocol against existing ones, thus confirming the value and potentiality of the approach

Architectures and Cross-Layer Design for Cognitive Networks

Network evolution towards self-aware autonomous adaptive networking attempts to overcome the ine?ciency of con?guring and managing networks, which leads to performance degradation. In order to optimize network operations, the introduction of self-awareness, self-management, and self-healing into the network was proposed. This created a new paradigm in networking, known as cognitive networking. This chapter describes state-of-the-art, as well as future directions in cognitive networking. Fundamental techniques for enabling cognitive properties, such as, adaptation, learning, and goal optimization processes are detailed in this text. A comparison of available research proposals leads to the design of a promising cognitive network architecture capable of incorporating cognitive network techniques. Finally, a discussion on the required properties of the cross-layer design for cognitive networks and deployment issues are speci?ed

Profiling Performance of Application Partitioning for Wearable Devices in Mobile Cloud and Fog Computing

Wearable devices have become essential in our daily activities. Due to battery constrains the use of computing, communication, and storage resources is limited. Mobile Cloud Computing (MCC) and the recently emerged Fog Computing (FC) paradigms unleash unprecedented opportunities to augment capabilities of wearables devices. Partitioning mobile applications and offloading computationally heavy tasks for execution to the cloud or edge of the network is the key. Offloading prolongs lifetime of the batteries and allows wearable devices to gain access to the rich and powerful set of computing and storage resources of the cloud/edge. In this paper, we experimentally evaluate and discuss rationale of application partitioning for MCC and FC. To experiment, we develop an Android-based application and benchmark energy and execution time performance of multiple partitioning scenarios. The results unveil architectural trade-offs that exist between the paradigms and devise guidelines for proper power management of service-centric Internet of Things (IoT) applications

Energy Efficient Data Collection in Opportunistic Mobile Crowdsensing Architectures for Smart Cities

Smart cities employ latest information and communication technologies to enhance services for citizens. Sensing is essential to monitor current status of infrastructures and the environment. In Mobile Crowdsensing (MCS), citizens participate in the sensing process contributing data with their mobile devices such as smartphones, tablets and wearables. To be effective, MCS systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and energy-efficient framework for data collection in opportunistic MCS architectures. Opportunistic sensing systems require minimal intervention from the user side as sensing decisions are application- or device-driven. The proposed framework minimizes the cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. We evaluate performance of the framework with simulations, performed in a real urban environment and with a large number of participants. The simulation results verify cost-effectiveness of the framework and assess efficiency of the data generation process

A Power Efficient Genetic Algorithm for Resource Allocation in Cloud Computing Data Centers

One of the main challenges in cloud computing is to increase the availability of computational resources, while minimizing system power consumption and operational expenses. This article introduces a power efficient resource allocation algorithm for tasks in cloud computing data centers. The developed approach is based on genetic algorithms which ensure performance and scalability to millions of tasks. Resource allocation is performed taking into account computational and networking requirements of tasks and optimizes task completion time and data center power consumption. The evaluation results, obtained using a dedicated open source genetic multi-objective framework called jMetal show that the developed approach is able to perform the static allocation of a large number of independent tasks on homogeneous single-core servers within the same data center with a quadratic time complexity