Survivability, the ability of a network to withstand and recover from failures, is one of the most important requirements of networks. Its importance is magnified in fiber optic networks with throughputs on the order of gigabits and terabits per second. This article takes a look at the techniques used to achieve survivability in traditional optical networks, and how those techniques are evolving to make next-generation WDM networks survivable.

In WDM optical networks, the physical layer impairments (PLIs) and their significance depend on network type-opaque, translucent, or transparent; the reach-access, metro, or core/long-haul; the number and type of network elements-fiber, wavelengths, amplifiers, switching elements, etc.; and the type of applications-real-time, non-real time, missioncritical, etc. In transparent optical networks, PLIs incurred by non-ideal optical transmission media accumulate along an optical path, and the overall effect determines the feasibility of the lightpaths. If the received signal quality is not within the receiver sensitivity threshold, the receiver may not be able to correctly detect the optical signal and this may result in high bit-error rates. Hence, it is important to understand various PLIs and their effect on optical feasibility, analytical models, and monitoring and mitigation techniques. Introducing optical transparency in the physical layer on one hand leads to a dynamic, flexible optical layer with the possibility of adding intelligence such as optical performance monitoring, fault management, etc. On the other hand, transparency reduces the possibility of client layer interaction with the optical layer at intermediate nodes along the path. This has an impact on network design, planning, control, and management. Hence, it is important to understand the techniques that provide PLI information to the control plane protocols and that use this information efficiently to compute feasible routes and wavelengths. The purpose of this article is to provide a comprehensive survey of various PLIs, their effects, and the available modeling and mitigation techniques. We then present a comprehensive survey of various PLI-aware network design techniques, regenerator placement algorithms, routing and wavelength assignment algorithms, and PLI-aware failure recovery algorithms. Furthermore, we identify several important research issues that need to be addressed to realize dynamically reconfigurable next-generation optical networks. We also argue the need for PLI-aware control plane protocol extensions and present several interesting issues that need to be considered in order for these extensions to be deployed in real-world networks.

Location-based services (LBS) have become an immensely valuable source of real-time information and guidance. Nonetheless, the potential abuse of users' sensitive personal data by an LBS server is evolving into a serious concern. Privacy concerns in LBS exist on two fronts: location privacy and query privacy. In this paper we investigate issues related to query privacy. In particular, we aim to prevent the LBS server from correlating the service attribute, e.g., bar/tavern, in the query to the user's real-world identity. Location obfuscation using spatial generalization aided by anonymization of LBS queries is a conventional means to this end. However, effectiveness of this technique would abate in continuous LBS scenarios, i.e., where users are moving and recurrently requesting for LBS. In this paper, we present a novel query-perturbation-based scheme that protects query privacy in continuous LBS even when user-identities are revealed. Unlike most exiting works, our scheme does not require the presence of a trusted third party.

Unmanned aerial vehicle (UAV)-empowered edge computing has been widely investigated in obstacle-free scenarios, where a moving UAV is in charge of handling offloaded singleton tasks from mobile devices on the ground. However, little attention has been paid to the scenario, in which the UAV serves a complex area with multiple obstacles and dependent tasks . A dependent task can be formulated as a directed acyclic graph (DAG) that contains a number of subtasks; and each subtask can be executed by a corresponding service function (SF) deployed on the UAV. In this backdrop, the joint UAV trajectory planning, DAG task scheduling, and SF deployment is formulated as an optimization problem in this article. Afterwards, a deep reinforcement learning (DRL)-based algorithm is presented to tackle the NP-hard problem. The state space, action space, and the reward function of the agent, i.e., the UAV, are defined, respectively, under the DRL framework. To evaluate the effectiveness of the proposal, a series of experiments is conducted with different parameter settings. Results show that the DRL-based algorithm performs much better than three heuristic algorithms in success rate of trajectory planning, the number of executed tasks, and the average task response latency.

Edge computing applications typically require generated data to be preprocessed at the source and then transmitted to an edge server. In such cases, transmission time and preprocessing time are coupled, yielding a tradeoff between them to achieve the targeted objective. This paper presents analysis of such a system with the objective of optimizing freshness of received data at the edge server. We model this system as two queues in tandem whose service times are independent but the transmission service time is monotonically dependent on the computation service time in mean value. This dependence captures the natural decrease in transmission time due to lower offloaded computation. We analyze various queue management schemes in this tandem queue where the compute queue has a single server, Poisson packet arrivals, general independent service and no extra buffer to save incoming packets. The transmit queue has a single server receiving packets from the compute queue with memoryless service time. We consider the transmit queue in two forms: (i) No data buffer and (ii) One unit data buffer and last come first serve with discarding. We analyze various non-preemptive as well as preemptive cases. We perform stationary distribution analysis and obtain closed form expressions for average age of information (AoI) and average peak AoI. Our numerical results illustrate analytical findings on how computation and transmission times could be traded off to optimize AoI and reveal a consequent tradeoff between average AoI and average peak AoI.

In this paper, we explore the potential of server waiting before packet transmission in improving the Age of Information (AoI) in status update systems. We consider a non-preemptive queue with Poisson arrivals and independent general service distribution and we incorporate waiting before serving in two packet management schemes: M/GI/1/1 and M/GI/1/2*. In M/GI/1/1 there is no data buffer and a packet is taken to server only when it is idle whereas in M/GI/1/2* there is a single unit buffer that captures the latest arriving status update packet. In both schemes, we introduce “server waiting”: In M/GI/1/1 scheme, the server waits for a deterministic time immediately after a packet enters the server. In M/GI/1/2* scheme, depending on queue state, the server waits for a deterministic time before starting service. In both cases, if a newer arrival is captured, existing packet is discarded with no reset of the waiting period. Different from most existing works, we analyze AoI evolution by indexing the incoming packets, which is enabled by an alternative method of partitioning the area under the evolution of instantaneous AoI to calculate its time average. We obtain expressions for average AoI and average peak AoI for both queueing disciplines with waiting. Our numerical results demonstrate that waiting before service can bring significant improvement in average age, particularly, for heavy-tailed service distributions. This improvement comes at the expense of an increase in average peak AoI. We highlight the trade-off between average and average peak AoI generated by waiting before serving.