Cloudlets, which refer to the edge computing services deployed at the proximity of end devices, are key providers of connectivity, storage, and computation resources to many applications. While access to cloudlets is pervasive in typical settings, it can be difficult in challenging, even hostile environments, such as military or post-disaster scenarios, featuring multi-hop communication and energy-constrained end devices. In these cases, cloudlets may have become the only equipment powerful enough to execute life-critical applications, such as battle-field situation awareness, tactic cooperation, and search-and-rescue missions. Quality of these services is greatly influenced by the minimum time that a packet can be delivered, i.e., the cloudlet access delay (CAD), whose characteristics remain unknown. To address the open question of fast and efficient cloudlet access, we establish a packet mobility model that allows CAD and energy consumption to be analyzed as a function of the initial device-cloudlet distance. We find that the expected CAD scales either linearly or quadratically under distinct types of packet mobility, and the successful access rate (SAR) can be bounded by functions of the delay constraint. Based on these findings, we develop a packet shedding algorithm that saves 24% transmission power, and reduces the average CAD by 2%, while maintaining a similar SAR in simulated cloudlet access environments.

Internet of Things (IoT) is a networking paradigm that interconnects physical systems to the cyber world, to provide automation and intelligence via interdependent links between the two domains. Such interdependence renders IoT systems vulnerable to random failures, e.g., broken communication links or crashed cyber instances, because a single incident in one domain can develop into a cascade-of-failures across domains, which dissolves the network structure, and has devastating consequences. To answer how robust an IoT system is, this paper studies its resilience by examining the impact of edge- and jointly-induced cascades, that is, a sequence of failures caused by randomly broken physical links (and simultaneous failing cyber nodes). Resilience of an IoT system is quantified by two new metrics, the critical edge disconnecting probability φcr, i.e., the maximum intensity of random failures the system can withstand, and the cascade length τcf, i.e., the lifetime of a cascade. For IoT systems with Poisson degree distributions, we derive exact solutions for the critical disconnecting probability φcr, above which an edge-induced cascade will completely fragment the network. We also find that the critical condition φcrmarks a dichotomy of the expected cascade length E(τcf): for the super-critical (φ > φcr) scenario, we obtain E(τcf) ~ exp(1 - φ) through analysis, while for the subcritical scenario, we observe E(τcf) ~ exp(1/1 - φ) through simulations. With these results, the final outcome of a cascade can be anticipated upon the initial failures, while the reaction window of time-sensitive countermeasures can be obtained before a cascade fully unfolds.

Internet of things (IoT) is expected to provide a fully informative and controllable environment that features networking, automation, and intelligence by interconnecting physical systems to cyber world. Such a correlation opens the interdependence between the two, upon which a single incident in one domain, e.g., a broken communication link, or an out-of-battery device, can cause a cascade-of-failures across physical and cyber domains. To understand the resilience of IoT systems against such detrimental cascades, this paper studies the aftermath of edge and jointly-induced cascades, that is, a sequence of failures induced by randomly broken physical links (and simultaneous failing cyber nodes) by answering how many nodes will survive the cascade with a newly defined node yield metric. Specifically, we construct a framework to establish self-consistent equations of node yield through an auxiliary graph, without requiring the exact network topology. Then two algorithms are proposed to numerically calculate node yield for interdependent networks with arbitrary degree distributions. For random graph with Poisson degree distributions, we prove the existence of a critical initial edge disconnecting probability φcr, under which an edge-induced cascade will result in dissolving the network topology, derive the closed form solution for φcr, and find that φcrincreases sub-linearly with the mean degree of the physical network.

The explosive number of IoT nodes and adoption of software-defined radio have enabled an efficient method of exploiting idle frequency spectrums called dynamic spectrum access (DSA). The foremost problem in DSA is for a pair of nodes to rendezvous and form a control channel prior to communication. Existing schemes require a channel hopping (CH) pattern with length O(N2), which is overly complex especially when the number of channels N is large. Moreover, the CH patterns are designed assuming DSA nodes have unlimited CH capability, which is hardly satisfied by nodes with long frequency switching time and limited sensing capacity. In this paper, we design a low-complexity rendezvous scheme that account for CH capability limits. The CH capability is captured using spectrum slice graphs that describe the possible channels for the next hop, given the currently-visited channel. By viewing the CH patterns as random walks over the spectrum graphs, we assign the walks with optimal transition probabilities that achieve the smallest rendezvous delay. The resulting symmetric random CH (S-RCH) scheme, which is suitable for IoT nodes without predetermined roles, achieves a lower rendezvous delay than existing Modular Modified Clock (MMC) scheme and offers more than 80 % successful rendezvous in mobile networks.

The ever-increasing traffic demand from social networking service (SNS) users and recent progress in device-to-device (D2D) technology have empowered a new D2D-based SNS paradigm, which enables multimedia content exchange via short-range wireless networking. In this paradigm, a small node failure may trigger a collection of rapidly-spreading isolation events called cascade-of-failures. Unlike existing works that studied the outcome of cascading failures from the spatial and probabilistic perspectives, this paper sheds light on the temporal properties of the cascade-of-failures. To do this, we introduce a maximum isolation time that quantifies the steps needed until the last node is isolated by the cascades, and then show that it scales non-monotonically to the fraction of initial survivors (non-failure nodes) and increases logarithmically with the network size. Then, we use the result to further analyze a node resilience metric, which is the likelihood that a node does not become isolated before its social networking session is finished. These findings, which are validated using numerical simulations, provide a temporal perspective of network performance that is valuable in the design of D2D-based SNSs yet still missing in the literature.

Device-to-device (D2D)-based mobile social networking (MSN) has been touted as a low-power, low-cost alternative to conventional MSN over cellular and WiFi networks. Nevertheless, the open nature of communication channels and the users' social activity that tend to decline over time expose D2D-based MSN to network contractions, which is a consequence of initial dropouts, combined with the jointly unsuccessful information delivery and social contacts. As a result, the network may found some of its users to vanish without knowing the cause of such dropouts. This, however, is a desirable knowledge that can guide towards the design of effective countermeasure schemes. In this paper, we study the problem of deciding whether initial dropouts are social or communication-induced, given the outcome of the network contractions. To do so, we define a minimum utility ratio that quantifies the worst-case impact of network contractions generated by initial dropouts. We derive self-consistent equations for computing the different minimum utility ratios caused by social and communication-based dropouts. We further show that this metric may exhibit different scaling order behaviors, depending on the root cause of initial dropouts. Finally, numerical results show that the proposed self-consistent equations and scaling order analysis can effectively distinguish social and communication-induced network contractions.

Motivated by the massive and increasing number of online messaging service users, the idea of utilizing shortrange device-to-device (D2D) communication has been adapted to the access of instant messaging services on-the-go, introducing a D2D-based messaging service (D2D-Msg) paradigm that promises higher data rate and longer battery life. The quality of message dissemination in such a new paradigm, however, remains largely unknown due to the open nature of the D2D environment. To address this, we define a node survival probability that captures the impact of random and targeted node failures due to the open wireless environment. Further, we define a secondary infection rate R* that measures how fast message propagates initially, and leverage a framework based on probability generating function to analyze R* under random and targeted failures. Numerical results show that the D2D-Msg is more robust against random failure, the targeted node failure favors communication graph with narrow degree distribution, and R* is proportional to the ratio between the number of message-receiving users to all users, which is a good metric for quantifying the D2D-Msg's robustness.

Device-to-device (D2D)-based social networking service (SNS) is an emerging information system that enables users with social ties to exchange multimedia contents through multihop short-range wireless links. In the D2D-based SNS, a random initial node failure may lead to a cascade of failures, which is a series of events in which users become isolated from others over subsequent time instances. Different from previous studies that analyze whether network-wide connectivity can be preserved after a cascade of failures, our study sheds light on the D2D-based SNS's resilience from the perspective of end-user connection experience. In this paper, we first introduce a numerical method for calculating the mean fraction of nodes that are not affected by the cascading failures and the amount of time to reach the end of such sequence of failures. Then, we apply a probabilistic approach to derive the lower and upper bounds of a node resilience metric, which is the likelihood that an end-user will not be isolated during an ongoing social networking session. Our analysis and numerical simulations indicate that, compared to exponentially-distributed session times, user session times with Pareto (heavy-tailed) distribution results in poorer node resilience, which quickly deteriorates when the mean session time is high.

Along with the explosive growth of mobile social network (MSN) users and the advent of device-to-device (D2D) communications, D2D-based MSN (D2D-MSN) has become a promising alternative for exchanging multimedia contents on-the-go. Although the complete structure of a D2D-MSN plays a key role in understanding its performance, such knowledge is not readily available due to the difficulty of collecting connectivity information from the vast amount of users. To model the structure, we define a D2D-MSN network that jointly captures the social connectivity over the MSN and the opportunistic D2D contacts among users. A random walk with self loop (RWSL) scheme that quickly converges to its stationary distribution is proposed to collect a subset of D2D-MSN nodes. An estimator is then introduced to obtain an unbiased estimate of the D2D-MSN graph's joint degree distribution, pi, j, from the set of visited nodes, leading to an unbiased RWSL scheme. The resulting estimate of pi, j can be used as a statistic for creating synthetic graph and generating functions for analyzing robustness of D2D-MSN. Numerical results show that the proposed unbiased RWSL converges faster to its stationary distribution, achieves higher joint degree distribution accuracy, and visits less number of nodes, compared to existing graph exploration schemes.

Although the research of mobile data networks has gained significant attention lately, little have been done to study the impact of finite network boundary in such networks. To understand how finite cell boundary affects the delay performance, we analyze the delay problem from the packet movement point of view. We first divide the network scenarios into three categories based on the value of the expected packet propagation speed v and derive the upper and lower bounds for their expected packet delay, respectively. Then, we show that the packet delay scales linearly when v0 and v = 0 when the boundary effect is absent. Simulation results verify our analysis and show that the boundary effect in fact increases the delay performance for both v > 0 and v = 0.

This paper deals with the power allocation problem in a wireless relay network employing differential modulation technique and Alamouti distributed space-time code, where signals from both the direct and relay paths are taken into account in the detection process. We propose a sub-optimal power allocation scheme based on the maximization of an approximate receive covariance expression. The proposed scheme jointly allocates the transmit power of both the source and relay nodes under a total transmit power and per-node transmit power constraints. We can increase the effectiveness of the proposed power allocation algorithm by grouping the relays with respect to their individual covariances. Numerical experiments show that the proposed covariance maximization algorithm combined with the relay grouping algorithm offers a respectable BER performance improvement, compared to the conventional schemes. Finally, we demonstrate that the proposed scheme requires only a small number of iterations to achieve convergence, making it attractive to practical systems.