Air components, including UAVs, planes, balloons, and satellites have been widely utilized since the fixed capacity of ground infrastructure cannot meet the dynamic load of the users. However, since those air components should be coordinated in order to achieve the desired quality of service, several next-generation paradigms have been defined including air computing. Nevertheless, even though many studies and open research issues exist for air computing, there are limited test environments that cannot satisfy the performance evaluation requirements of the dynamic environment. Therefore, in this study, we introduce our discrete event simulator, AirCompSim, which fulfills an air computing environment considering dynamically changing requirements, loads, and capacities through its modular structure. To show its capabilities, a dynamic capacity enhancement scenario is used to investigate the effect of the number of users, UAV s, and requirements of different application types on the average task success rate, service time, and server utilization. The results demonstrate that AirCompSim can be used for experiments in air computing.

The Internet of Vehicles (IoV) vision encompasses a wide range of novel intelligent highway scenarios that rely on vehicles with an ever-increasing degree of autonomy and the prospect of sophisticated services like e-Horizon and cognitive driving assistance. The self-driving vehicle, on the other hand, entails a new passenger profile where sophisticated infotainment applications are expected to enhance the quality of travel. From the technical stand point, for this vision to become a reality a streamlined edge computing infrastructure, namely Vehicular Edge Computing (VEC), is required where computationally intensive workloads are offloaded to a nearby VEC infrastructure. However, the highly dynamic environment renders it difficult to efficiently operate a VEC system to yield the crisp performance required on an autonomous vehicle. In this setting, where to offload each task stands out as a crucial decision problem, and the conventional methods prove insufficient for its solution. In our work, we proposed a two-stage machine learning-based vehicular edge orchestrator which takes into account not only the task completion success but also the service time. To demonstrate how our approach performs in a realistic setting, we employed EdgeCloudSim to design extensive experiments where the characteristics of the vehicular applications, upload/download sizes, computational footprints of the tasks, the LAN, MAN and WAN network models, and the mobility are considered. Detailed performance evaluation of the proposed system via simulation is carried out where both overall and service type-specific performance scores in comparison with opponent schemes are reported.

Edge computing is based on the philosophy that the data should be processed within the locality of its source. Edge computing is entering a new phase where it gains wide acceptance from both academia and the industry as the commercial deployments are starting. Edge of the network presents a very dynamic environment with many devices, intermittent traffic, high mobility of the end user, heterogeneous applications and their requirements. In this scene, scalable and efficient management and orchestration remains to be a problem. We focus on the workload orchestration problem in which execution locations for incoming tasks from mobile devices are decided within an edge computing infrastructure, including the global cloud as well. Workload orchestration is an intrinsically hard, online problem. We employ a fuzzy logic-based approach to solve this problem by capturing the intuition of a real-world administrator to get an automated management system. Our approach takes into consideration the properties of the offloaded task as well as the current state of the computational and networking resources. Detailed set of experiments are designed with EdgeCloudSim to demonstrate the competitive performance of our approach for different service classes.

In addition to traditional network services, it is now possible to realize innovative services through the integration of small-form smart devices into our daily lives. Although the set of modern end-user devices have a certain processing power, the execution of the service routines may be handled by remote servers due to battery limitation and other capacity restrictions. The traditional network infrastructure and location- centric communication mechanisms provided by the TCP/IP protocol stack have become inadequate for orchestrating these highly-dynamic operations in a smooth manner. Therefore, it is envisioned that the network structure should be service-oriented to decouple the service instances from their locations. In order to leverage the service-centric model within the network, this study proposes a programmable network solution based on P4. The design of the solution is discussed with the P4 terminology and the implementation details are presented with a demonstrative example to validate the applicability of the solution. The fully-programmable P4 environment enables to facilitate a long-term solution that is compatible with the current network infrastructure and TCP/IP protocol stack.

The current developments in smart devices, wearable gadgets and IoT (Internet-of-Things) are triggering a variety of novel use cases and services. Once the technology matures, a wide range of services is expected to be provided at the edge of the network in coordination with the cloud computing infrastructure. These services will be highly dynamic meaning that they can be served from edge computing facilities and from central cloud servers being transferred back and forth. Also, considering the mobility of the users and the varying demand, those services might need to be live migrated between nearby edge servers on-the-fly. This setup creates an environment that needs to be transparent to the end users. The current legacy networking paradigm, however, allow services to be reached by IP and port addresses, not by their content. Alternatively, in the service-centric approach the services themselves are handled independent of their location and the focus shifts to “what ” instead of “where ”. Due to the complexities involved, it is not a straightforward task to establish service-centricity at the edge scenarios. As a remedy, this paper proposes a service-centric approach at the edge servers using orchestration capabilities offered by the Software-Defined Networking (SDN) technology. To demonstrate how SDN can help to alleviate the problem, an emulation environment is used in which northbound applications are implemented in order to setup the service-centric structure. The effect of service-centric approach is shown with the load balancing experiments where the performance of the proposed system is evaluated for various use cases.

Visual capability introduced to Wireless Sensor Networks (WSNs) render many novel applications that would otherwise be infeasible. However, unlike legacy WSNs which are commercially deployed in applications, visual sensor networks create additional research problems that delays the real-world implementations. Conveying real-time video streams over resource constrained sensor hardware remains to be a challenging task. As a remedy, we propose a fairness-based approach to enhance the event reporting and detection performance of the Video Surveillance Sensor Networks. Instead of achieving fairness only for flows or for nodes as investigated in the literature, we concentrate on the whole application requirement. Accordingly, our Event-Based Fairness (EBF) scheme aims at fair resource allocation for the application level messaging units called events. We identify the crucial network-wide resources as the in-queue processing turn of the frames and the channel access opportunities of the nodes. We show that fair treatment of events, as opposed to regular flow of frames, results in enhanced performance in terms of the number of frames reported per event and the reporting latency. EBF is a robust mechanism that can be used as a stand-alone or as a complementary method to other possible performance enhancement methods for video sensor networks implemented at other communication layers.

Wireless Sensor Networks (WSNs) give rise to a new networking paradigm in which energy efficiency is a high priority goal. A direct measure of the energy efficiency is the network lifetime which WSN proposals strive to extend. To correctly quantify the lifetime, the metric must be defined in an application dependent manner. In this paper, we propose a generic lifetime measurement framework called Weighted Cumulative Operational Time (WCOT) for the performance evaluation of the WSNs. Novelty brought by WCOT is twofold: First, it defines a utility based interface for the diverse WSN applications to incorporate their scenario specific requirements into the metric itself. Second, WCOT assigns different weights to the operational durations that have different utilities and perform a weighted summation to calculate the cumulative lifetime thereafter. With this mechanism, a more representative lifetime metric which maps the complete network behavior into a numeric value is obtained. This is in contrast with metrics which focus solely on certain milestones of the network functionality to quantify the lifetime which include the first node death, the last node death.