A Profitable and Energy-Efficient Cooperative Fog Solution for IoT Services

Fog-to-Fog (F2F) communication has been introduced to deliver services to clients with minimal reliance on the cloud through resource and capability sharing of cooperative fogs. Current solutions assume full cooperation among the fogs to deliver simple and composite services. Realistically, each fog might belong to a different network operator or service provider and thus will not participate in any form of collaboration unless self-monetary profit is incurred. In this paper, we introduce a fog collaboration approach for simple and complex multimedia service delivery to cloud subscribers while achieving shared profit gains for the cooperating fogs. The proposed work dynamically creates short-term service-level-agreements (SLA) offered to cloud subscribers for service delivery while maximizing user satisfaction and fog profit gains. The solution provides a learning mechanism that relies on online and offline simulation results to build guaranteed workflows for new service requests. The configuration parameters of the short-term SLAs are obtained using a modified tabu-based search mechanism that uses previous solutions when selecting new optimal choices. Performance evaluation results demonstrate significant gains in terms of service delivery success rate, service quality, reduced power consumption for fog and cloud datacenters, and increased fog profits

Providing Secure and Reliable Communication for Next Generation Networks in Smart Cities

Finding a framework that provides continuous, reliable, secure and sustainable diversified smart city services proves to be challenging in today’s traditional cloud centralized solutions. This article envisions a Mobile Edge Computing (MEC) solution that enables node collaboration among IoT devices to provide reliable and secure communication between devices and the fog layer on one hand, and the fog layer and the cloud layer on the other hand. The solution assumes that collaboration is determined based on nodes’ resource capabilities and cooperation willingness. Resource capabilities are defined using ontologies, while willingness to cooperate is described using a three-factor node criteria, namely: nature, attitude and awareness. A learning method is adopted to identify candidates for the service composition and delivery process. We show that the system does not require extensive training for services to be delivered correct and accurate. The proposed solution reduces the amount of unnecessary traffic flow to and from the edge, by relying on nodeto-node communication protocols. Communication to the fog andcloud layers is used for more data and computing-extensive applications, hence, ensuring secure communication protocols to the cloud. Preliminary simulations are conducted to showcase the effectiveness of adapting the proposed framework to achieve smart city sustainability through service reliability and security. Results show that the proposed solution outperforms other semicooperative and non-cooperative service composition techniques in terms of efficient service delivery and composition delay, service hit ratio, and suspicious node identification

PriNergy: A Priority-based Energy Efficient Routing Method for IoT Systems

The Internet of Things (IoT) devices gather a plethora of data by sensing and monitoring the surrounding environment. Transmission of collected data from the IoT devices to the cloud through relay nodes is one of the many challenges that arise from IoT systems. Fault tolerance, security, energy consumption and load balancing are all examples of issues revolving around data transmissions. This paper focuses on energy consumption, where a priority-based and energy-efficient routing (PriNergy) method is proposed. The method is based on the routing protocol for low-power and lossy network (RPL) model, which determines routing through contents. Each network slot uses timing patterns when sending data to the destination, while considering network traffic, audio and image data. This technique increases the robustness of the routing protocol and ultimately prevents congestion. Experimental results demonstrate that the proposed PriNergy method reduces overhead on the mesh, end-to-end delay and energy consumption. Moreover, it outperforms one of the most successful routing methods in an IoT environment, namely the quality of service RPL (QRPL)

Cloud-Based Multi-Agent Cooperation for IoT Devices Using Workflow-Nets

Most Internet of Things (IoT)-based service requests require excessive computation which exceeds an IoT device's capabilities. Cloud-based solutions were introduced to outsource most of the computation to the data center. The integration of multi-agent IoT systems with cloud computing technology makes it possible to provide faster, more efficient and real-time solutions. Multi-agent cooperation for distributed systems such as fog-based cloud computing has gained popularity in contemporary research areas such as service composition and IoT robotic systems. Enhanced cloud computing performance gains and fog site load distribution are direct achievements of such cooperation. In this article, we pro- pose a work ow-net based framework for agent cooperation to enable collaboration among fog computing devices and form a cooperative IoT service delivery system. A cooperation operator is used to find the topology and structure of the resulting cooperative set of fog computing agents. The operator shifts the problem defined as a set of work ow-nets into algebraic representations to provide a mechanism for solving the optimization problem mathematically. IoT device resource and collaboration capabilities are properties which are considered in the selection process of the cooperating IoT agents from di_erent fog computing sites. Experimental results in the form of simulation and implementation show that the cooperation process increases the number of achieved tasks and is performed in a timely manner

Data caching and selection in 5G networks using F2F communication

As an emergent technology the IoT promises to harness the computational and data resources distributed across different remote clouds. Fog computing extends cloud computing by bringing the network and cloud resources closer to the network edge. As the number of resources contributing to the cloud/fog system grows, so the problems associated with efficient and effective resource selection and allocation. In this paper, we introduce a fog-to-fog (F2F) data caching and selection method, which allows IoT devices to retrieve data in a faster and more efficient way. The proposed solution is based on a data caching and selection strategy using a multi-agent cooperation framework. Caching is achieved by decomposing cloud data into a set of files and then placed into fog storage sites. The selection process is based on a run-time file location prediction technique, which collects and maintains a repository of fog data in the form of log files. When data needs to be retrieved, prediction is made with the aid of these logs and previous successful search queries resulting in realistic run-time location estimates as well as best fog selection. Simulation results showcase the reduced data retrieval latency that enable tactile Internet in 5G. Additionally, results show increased successful file hit ratio leading to a reduced number of repeated downloads