Mobile Edge Computing (MEC) provides the radio access networks with cloud computing capabilities to fulfill the requirements of the Internet of Things (IoT) services such as high reliability and low latency. Offloading services to edge servers can alleviate the storage and computing limitations and prolong the lifetimes of the IoT devices. However, offloading in MEC faces scalability problems due to the massive number of IoT devices. In this article, we present a new integration architecture of the cloud, MEC and IoT, and propose a lightweight request and admission framework to resolve the scalability problem. Without coordination among devices, the proposed framework can be operated at the IoT devices and computing servers separately, by encapsulating latency requirements in offloading requests. Then, a selective offloading scheme is designed to minimize the energy consumption of devices, where the signalling overhead can be further reduced by enabling the devices to be selfnominated or self-denied for offloading. Simulation results show that our proposed selective offloading scheme can satisfy the latency requirements of different services and reduce the energy consumption of the IoT devices.

In this paper we introduce a hierarchical system model that captures the decision making processes involved in a network of multiple providers and a large number of consumers in the smart grid, incorporating multiple processes from power generation to market activities and to power consumption. We establish a Stackelberg game between providers and end-users, where the providers behave as leaders maximizing their profit, and end-users act as followers maximizing their individual welfare. We obtain closed-form expressions for the Stackelberg equilibrium of the game and prove that a unique equilibrium solution exists. In the large population regime, we show that higher number of providers help improve profits for the providers. This is inline with the goal of facilitating multiple distributed power generation units, one of the main design considerations in the smart grid. We further prove that there exists a unique number of providers that maximize their profits, and develop an iterative and distributed algorithm to obtain it. Finally, we provide numerical examples to illustrate the solutions and to corroborate the results.

Vehicle-to-Grid (V2G) system with efficient Demand Response Management (DRM) is critical to solve the problem of supplying electricity by utilizing surplus electricity available at EVs. An incentivilized DRM approach is studied to reduce the system cost and maintain the system stability. EVs are motivated with dynamic pricing determined by the group-selling based auction. In the proposed approach, a number of aggregators sit on the first level auction responsible to communicate with a group of EVs. EVs as bidders consider Quality of Energy (QoE) requirements and report interests and decisions on the bidding process coordinated by the associated aggregator. Auction winners are determined based on the bidding prices and the amount of electricity sold by the EV bidders. We investigate the impact of the proposed mechanism on the system performance with maximum feedback power constraints of aggregators. The designed mechanism is proven to have essential economic properties. Simulation results indicate the proposed mechanism can reduce the system cost and offer EVs significant incentives to participate in the V2G DRM operation.

Demand Response Management (DRM) is a key component in the smart grid to effectively reduce power generation costs and user bills. However, it has been an open issue to address the DRM problem in a network of multiple utility companies and consumers where every entity is concerned about maximizing its own benefit. In this paper, we propose a Stackelberg game between utility companies and end-users to maximize the revenue of each utility company and the payoff of each user. We derive analytical results for the Stackelberg equilibrium of the game and prove that a unique solution exists. We develop a distributed algorithm which converges to the equilibrium with only local information available for both utility companies and end-users. Though DRM helps to facilitate the reliability of power supply, the smart grid can be succeptible to privacy and security issues because of communication links between the utility companies and the consumers. We study the impact of an attacker who can manipulate the price information from the utility companies. We also propose a scheme based on the concept of shared reserve power to improve the grid reliability and ensure its dependability.

Cooperative spectrum sensing improves the relia- bility of detection. However, if the secondary users are selfish, they may not collaborate for sensing. In order to address this problem, Medium Access Control (MAC) protocols can be designed to enforce cooperation among secondary users for spectrum sensing. In this paper, we investigate this problem using game theoretical framework. We introduce the concept of correlated equilibrium for the cooperative spectrum sensing game among non-cooperative secondary users and formulate the optimization problem for the case where secondary users have heterogeneous traffic dynamics. We show that the correlated equilibrium improves the system utility, as compared to the mixed strategy Nash equilibrium. While maximizing system payoff is important, fairness is also equally important in systems with dissimilar users. In order to address fairness issue, we propose a new fair social welfare correlated equilibrium, which maximizes the system utility and ensures that the less well-off users do not starve. We employ a no-regret learning algorithm for distributed implementation of the correlated equilibrium. Finally, we propose a neighbourhood based learning algorithm and show that it achieves better performance than the no-regret algorithm.

Real time traffic such as voice and video have strict requirements on the acceptable end-to-end packet delay. When there are different types of traffic with different requirements on tolerable latency, priority based packet scheduling schemes are normally used in order to reduce the queuing delay for real time services. However, in cognitive radio networks, the time that the system spends on spectrum sensing adds further delay to the packet transmission. In this paper, we propose a new scheme to significantly reduce the overall packet delay, including the delay due to sensing for real time services in cognitive radio networks. We derive the expression for average packet delay for the proposed scheme and the simulation results match well with the analytical results. The numerical results show that the priority based scheduling scheme combined with our scheme substantially reduces the packet delay for real time applications.

As the Internet takes an increasingly central role in our communications infrastructure, the slow convergence of routing protocols after a network failure becomes a growing problem. To assure fast recovery from link and node failures in IP networks, we present a new recovery scheme called Multiple Routing Configurations (MRC). Our proposed scheme guarantees recovery in all single failure scenarios, using a single mechanism to handle both link and node failures, and without knowing the root cause of the failure. MRC is strictly connectionless, and assumes only destination based hop-by-hop forwarding. MRC is based on keeping additional routing information in the routers, and allows packet forwarding to continue on an alternative output link immediately after the detection of a failure. It can be implemented with only minor changes to existing solutions. In this paper we present MRC, and analyze its performance with respect to scalability, backup path lengths, and load distribution after a failure. We also show how an estimate of the traffic demands in the network can be used to improve the distribution of the recovered traffic, and thus reduce the chances of congestion when MRC is used.

An important challenge in Optical Burst Switching (OBS) is to reduce the burst loss caused by contention. In this paper we develop a new method that improves the burst loss probability by burst re-routing in case of contention. Our re-routing method guarantees that bursts (that are not discarded) are routed towards the egress on a loop-free path. We have made a detailed discrete event simulation model of OBS networks and evaluated the performance of our new method by comparing it to popular deflection methods. We simulate several realistic network topologies and find that, for realistic loads, re-routing indeed improves burst loss probability and has clear advantages when compared to the discussed deflection methods.

Recovery at the IP layer has originally been handled by the slow process of IP re-convergence. As the dependence on the Internet broadens and real time applications like VoIP become a common service of the Internet, fast proactive recovery becomes an important property of the communication protocols. There are currently two IETF initiatives for proactive recovery drawing considerable attention, IP Fast Reroute and Multi-Topology Routing using Multiple Routing Configurations. In this paper we evaluate and compare these approaches.

Fast proactive recovery has for years been a very import- ant research field due to increased reliability in the Internet for business critical and real-time communications. Resilient Routing Layers (RRL) and Multiple Routing Configurations (MRC) has been proposed as a new approach providing local and proactive recovery. RRL and MRC also provides a network manager with a simple set of sub-topologies as abstractions for the recovery routing. This paper will compare different methods for generating such layers and configurations along two para- meters, scalability and effects on routing of the recovered traffic.

Resilient Packet Ring (RPR) is a new standard, designated IEEE standard number 802.17, for MAN and WAN dual ring topologies. RPR uses the buffer insertion principle as a basis for its medium access control protocol. In this paper, we analyze parts of the aggressive mode of the RPR fairness protocol. We look at a congested node, and utilize control systems theory to analyze the stability of the associated fairness algorithm. In particular, we discuss how the settings of the two important parameters ageCoef and lpCoef influence the stability of an RPR-network. At the end of the paper we present simulated scenarios in order to illustrate our results.

Resilient Packet Ring (RPR, IEEE 802.17) is designed with a protection mechanism aiming at restoring traffic on the ring within 50 ms in case of a link or station failure. In this article, we evaluate RPR protection with respect to service disruption, packet reordering and packet loss. Different error scenarios are simulated, with both steering and wrapping protection. Unfortunately, the 50 ms restoration time guarantee can not always be met if in order delivery of packets is required, since RPR uses a quite long (default 40 ms) topology stabilisation period to avoid packet reordering. We suggest a novel protection mechanism, that does not have to wait for the new topology to be stable, and that gives sub 50 ms restoration for all traffic. We also show that for in order delivery of packets, our new mechanisms discards a very low number of packets compared to the mechanism of the RPR standard.

The IEEE Working group P802.17 is standardizing a new ring topology network architecture, called the Resilient Packet Ring (RPR), to be used mainly in metropolitan and wide area networks. This paper presents a technology background, gives an overview, and explains many of the design choices the RPR working group faced during the development of the standard. Some major architectural features are illustrated and compared by showing performance evaluation results using the RPR simulator developed at Simula Research Laboratory using the OPNET Modeler simulation environment.