Summary Federated edge cloud (FEC) is an edge computing environment where servers in the same edge management domain could collaborate to handle latency‐sensitive services, thus better guaranteeing users' requirements on multiple quality of service (MQoS). Traditional scheduling methods only consider whether the server meets the resource requirements of the service, without paying attention to whether their resource characteristics match. In scenarios where server's resources are dynamically changing, this may reduce the resource utilization and the efficiency of service execution. To address this challenge, a dynamic resource adaptation‐multiple quality of service (DRA‐MQoS) algorithm is proposed for service scheduling in this environment. DRA‐MQoS could dynamically evaluate the resource characteristics of servers and services from the perspectives of “individual” and “overall” by combining the historical scheduling data of services and the utilization of different resources of server clusters. By scheduling the services to servers with the same resource characteristics for execution, the proposed policy fusion algorithm efficiently responds to the dynamically changing quality of service (QoS) demands of users by changing the weight parameters of policies. Simulation results in CloudSimSDN show that the energy consumption and execution time of DRA‐MQoS are reduced by 23% and 12%, respectively, compared with existing methods.

As the explosion of Internet traffic is quickly leading to overloaded cellular network, device-to-device (D2D)-based content sharing is proposed as a method to offload mobile data traffic. The performance of D2D-based content sharing is dramatically affected by the success rate of content fetching from nearby devices and quality of content transmission, which is determined by the geographic distribution of mobile devices, the number of devices having contents in their caches, and the condition of D2D links. Hence, a key problem is how to cache various contents in the limited storage of mobile devices for improving the success rate of content fetching. In this paper, we aim to design a caching policy by considering the joint impact of locality of real-world mobile data traffic and device contact pattern to improve the success rate of content fetching. To do this, we first study the characteristics of network traffic and device contact pattern by analyzing traces from realistic networks. Then, we design a locality-based caching policy and derive the content caching probability and hit ratio through mathematical analysis. Through numerical evaluation and trace-driven simulations, we not only quantify how content popularity, content active lifetime, content size, content bit rate, device storage, transmission rate, and closeness centrality affect the content hit ratio, but also provide comparison on hit ratio and storage cost in different caching policy, which is a strong evidence that the joint impacts from characteristics of content and device are the necessary consideration when to design a caching policy.

There have been extensive works on the design of opportunistic spectrum access and routing schemes to improve spectrum efficiency in cognitive radio networks (CRNs), which becomes an integral component in the future communication regime. Nonetheless, the potentials of CRNs in boosting network performance yet remain to be explored to reach the full benefits of such a phenomenal technique. In this paper, we study the end-to-end latency in CRNs in order to find the sufficient and necessary conditions for real-time applications in finite networks and large-scale deployments. We first provide a general mobility framework which captures most characteristics of the existing mobility models and takes spatial heterogeneity into account. Under this general mobility framework, secondary users are mobile with an mobility radius a, which indicates how far a mobile node can reach in spatial domain. We find that there exists a cutoff point on a, below which the latency has a heavy tail and above which the tail of the latency is bounded by some Gamma distributions. As the network grows large, the latency is asymptotically scalable (linear) with respect to the dissemination distance (e.g., the number of hops or euclidean distance). An interesting observation is that although the density of primary users adversely impacts the expected latency, it makes no influence on the dichotomy of the latency tail in finite networks and the linearity of latency in large networks. Our results encourage CRN deployment for real-time and large applications, when the mobility radius of secondary users is large enough.

With the popularity of smartphones and explosion of mobile applications, mobile devices become the prevalent computing platform for convenient communication and rich entertainment. Mobile cloud computing (MCC) is proposed to overcome the limited resources of mobile systems. However, when users access MCC through wireless networks, cellular network is likely to be overloaded and Wi-Fi connectivity is intermittent. Therefore, device-to-device (D2D) communication is exploited as an alternative for MCC. An important issue in exploring D2D communication for MCC is how users can detect and utilize the computing resources on other mobile devices. In this paper, we propose two mobile cloud access schemes: optimal and periodic access schemes, and study the corresponding performance of mobile cloud computing (i.e., mobile cloud size, node's serviceable time percentage, and task success rate). We find that optimally, node's serviceable time percentage and task success rate approach 1. Using more practical periodic access scheme, node's serviceable time percentage and task success rate are determined by the ratio of contact and inter-contact time between two nodes.

A mobile cloudlet is a set of resource-rich mobile devices - referred to as cloudlet nodes - that an initiator mobile device can connect to for services. In this paper, we examine the fundamental mobile cloudlet properties that unfold whether and when a mobile cloudlet can provide mobile application service. Specifically, we investigate the cloudlet size, cloudlet node's lifetime and reachable time. Traces and mathematical analysis demonstrate that 1) the more frequently mobile devices meet, the larger the pool of computing resources an initiator can access; 2) intermittent connection between devices has little adverse effect on the optimal computing performance of mobile cloudlet in the long run; 3) the ratio E(TC)/[E(TI)+E(TC)] indicates the connection likelihood of an initiator and a cloudlet node (i.e., reachability of the cloudlet node), where TCand TIare their contact and inter-contact time. We further derive upper and lower bounds on computing capacity and computing speed of a mobile cloudlet. An initiator can use both bounds to decide whether to offload its task to remote clouds or local mobile cloudlets for better mobile application services.

Device-to-device (D2D) communications enable direct communications and information distribution among closely located devices in wireless networks. Many applications of D2D communications require message dissemination to a group of mobile users at certain locations. The challenges of message dissemination come from highly dynamic network environments due to movements of devices. Existing studies on message dissemination have focused on information propagation speed and latency, but the size of the area affected by message dissemination at time t is also critical to D2D communication applications that heavily depend on message dissemination among users in a geographic region. In this paper, we study the fundamental issues in D2D communications: how far the message dissemination can reach by time t (referred as dissemination distance) and how long the dissemination takes to inform nodes located at distanced (referred to as hitting time), especially in dynamic, intermittently connected networks. We first derive analytic bounds of dissemination distance and hitting time under different dissemination mechanisms, providing the spatial and temporal limits of message dissemination. Analytic results are further validated by simulation results of several corresponding dissemination algorithms. Finally, two application scenarios are provided to illustrate how our results serve as guidelines to choose or design appropriate dissemination methods for different D2D communication applications.

Vehicular ad hoc network (VANET) is one of the most promising large-scale applications of mobile ad hoc networks. VANET applications are rooted in advanced understanding of communication networks because both control messages and data information need to be disseminated in geographic regions (i.e., Geocast). The challenges come from highly dynamic environments in VANET. Destination nodes in geocast are dynamic over time due to vehicle mobility, which undermines existing results on dissemination latency and information propagation speed with pre-determined destinations. Moreover, the affected area by the dissemination, which is referred to as horizon of message (HOM), is critical in geocast as it determines the latency for the message reaching nodes inside the area of interest (AOI), in which the message is relevant to drivers. Therefore, we characterize the HOM in geocast by how far the message can reach within time t (referred as dissemination distance) and how long the message takes to inform nodes at certain locations (referred as hitting time). Analytic bounds of dissemination distance and hitting time are derived under four types of dissemination mechanisms, which provide insights into the spatial and temporal limits of HOM as well as how the numbers of disseminators and geographic information exchanges affect them. Applying analytic and simulation results to two real applications, we observe that geocast with AOI near the source or high reliability requirement should recruit multiple disseminators while geocast with AOI far from the source need to utilize geographic information for fast message propagation.

In many Vehicular Ad-hoc Network applications, such as hazard warning and traffic coordination, the message dissemination in unreliable and highly mobile network environment is a key challenge. In order to understand the relationship between dissemination latency and reliability, we analyze the latency of gaining α-reliability that a node correctly receives a message with probability larger than α (0min(α). Besides dissemination reliability requirement α, tmin(α) also depends on node's original distance from the source, node mobility, channel reliability, and traffic flow. Numerical analysis discloses several interesting insights that 1) tmin(α) is dominated by the first attempt to send the message to a destination, 2) node mobility has little impact on tmin(α) in emergency information dissemination, and 3) transmission range and node density greatly affect dissemination latency and reliability.

Vehicular Adhoc Networks (VANETs) aim to improve road safety and convenience through vehicle-to-vehicle and vehicle-to-roadside communications. Traffic information and accident warnings are often disseminated to vehicles in certain areas where driving could be affected by hazardous situations. Such message dissemination with destinations confined in specific geographic regions is referred to as Geo-Dissemination. In this paper, we analyze how far a geo-dissemination can possibly reach over a period of time t (denoted as dissemination distance D(t)), and what is the latency for a message to reach locations that are d distance far from the source (denoted as the stopping time τ). Simulations results of two dissemination methods (stateless opportunistic forwarding and GPS-based message broadcasting) are compared with our analytic results.

The radio link between a pair of wireless nodes is determined by radio channels, transmission range, node mobility and node-pair distance, which form a set of random factors in multihop wireless networks. The properties of such radio links can be characterized by link lifetime, residual link lifetime and link change rate, which, in fact, have been widely used for network design and performance evaluation. In this paper, we take a new modeling approach that captures the dynamics of radio channels and node movements in small-scale. More specifically, distance transition probability matrix is designed in order to describe the joint effects of dynamic transmission range due to radio channel fading and relative distance of a node-pair resulting from random movements. We find that the PDF of link lifetime can be approximated by an exponential distribution with parameter characterized by the ratio of average node speed \bar{V} to effective transmission range R_e. To further understand the implication of link properties, analytical results are used to investigate the upper bound of network connectivity and the associated network performance is evaluated by extensive simulations.

Vehicular Adhoc Networks (VANETs) are dedicated to improve the safety and efficiency of transportation systems through vehicle to vehicle or vehicle to road side communications. VANETs exhibit dynamic topology and intermittent connectivity due to high vehicle mobility. These distinguished features declare a challenging question: how to detect on the fly vehicular networks such that we can explore mobility-assisted message dissemination and topology control in VANETs. As being closely related to network dynamics, vehicle mobility could be explored to uncover network structure. In this paper, we have observed that mobility of vehicle, rather than being random, shows temporal locality (i.e., frequently visiting several communities like home and office), and spatial locality (i.e., velocity constrained by road layout and nearby vehicles). We first examine temporal locality using a campus trace, then measure temporal locality similarity between two vehicles based on the relative entropy of their location preferences. By further incorporating spatial locality similarity, we introduce a new metric, namely dual locality ratio (DLR), which represents the mobility correlation of vehicles. Simulation results show that DLR can effectively identify dynamic vehicular network structures. We also demonstrate applications of DLR for improving performances of data forwarding and clustering in vehicle-to-vehicle networks.