Cloud computing enables larger classes of application service providers to distribute their services to world-wide users in multiple regions without their own private data centers. Heterogeneity and resource limitation of geo-graphically distributed cloud data centers impose application service providers to have incentives to optimize their computing resource usage while guaranteeing some level of quality of service. Recent studies proposed various techniques for optimization of computing resource usage from cloud users (or application service providers) perspective with little consideration of competition. In addition, optimization efforts of application service providers motivate cloud service providers owning multiple geo-distributed clouds to decide their computing resource prices considering their efforts. In this context, we formulate this problem for cloud service providers as a game of resource pricing in geo-distributed clouds. One of the main challenges in this problem is how to model the best responses of application service providers, given resource price information of clouds in non-overlapped regions. We propose a novel concave game to describe the quantity competition among application service providers reducing payment while guaranteeing fair service delay to end users. Furthermore, we optimize the prices of computing resources to converge to the equilibrium. In addition, we show several characteristics of the equilibrium point and discuss their implications to design computing resource markets for geo-distributed clouds.

Recently, a majority of security operations centers (SOCs) have been facing a critical issue of increased adoption of transport layer security (TLS) encryption on the Internet, in network traffic analysis (NTA). To this end, in this survey article, we present existing research on NTA and related areas, primarily focusing on TLS-encrypted traffic to detect and classify malicious traffic with deployment scenarios for SOCs. Security experts in SOCs and researchers in academia can obtain useful information from our survey, as the main focus of our survey is NTA methods applicable to malware detection and family classification. Especially, we have discussed pros and cons of three main deployment models for encrypted NTA: TLS interception, inspection using cryptographic functions, and passive inspection without decryption. In addition, we have discussed the state-of-the-art methods in TLS-encrypted NTA for each component of a machine learning pipeline, typically used in the state-of-the-art methods.

By collecting data from sensor devices, wireless sensor networks enable consumer product management in indoor environments. However, most off-the-shelf sensor devices are battery-powered and hence hampered by the limitation of battery life. In this context, wireless rechargeable sensor networks (WRSNs) which adopt wireless power transfer (WPT) technique - leveraging electromagnetic waves for sensor energy replenishment - have emerged as a promising scenario for the architecture of self-sustainable and resilient sensor networks. Nevertheless, while the rapid proliferation of studies on discussion WPT and data collection in WRSNs has been witnessed in recent years, their unilateral investigation makes them insufficient to construct high-performance WRSNs. Therefore, this paper firstly explores joint WPT and data collection in a WRSN and accordingly presents a medium access control protocol called FarMac. The WRSN employs multiple sink nodes which can either conduct WPT to or data collection from numerous deployed sensor devices. The sensor devices, on the other hand, fall into two categories: lethargic sensor device which needs energy replenishment from the sink nodes before conveying its data and energetic sensor device which can disseminate its data directly. In this context, FarMac leverages a centralized algorithm to achieve multisource WPT for maximizing the transferred power to a lethargic sensor device. In addition, each lethargic sensor device executes a distributed algorithm to compute its necessary energy harvesting time. Furthermore, FarMac achieves concurrent WPT and data collection through interference cancellation technique. Simulation results demonstrate that FarMac improves network throughput by up to 41% compared with a benchmark approach and guarantees network resilience ¹.

Topology control is to determine the transmission power of each node so as to maintain network connectivity and consume the minimum transmission power. Cooperative Communication (CC) is a new technology that allows multiple nodes to simultaneously transmit the same data. It can save transmission power and extend transmission coverage. However, prior research work on topology control considers CC only in the aspect of energy saving, not that of coverage extension. We observe that CC can bridge (link) disconnected networks and therefore identify the challenges in the development of a centralized topology control scheme, named shape Cooperative Bridges, which reduces transmission power of nodes as well as increases network connectivity. We propose three algorithms that select energy efficient neighbor nodes, which assist a source node to communicate with a destination node: an optimal method and two greedy heuristics. In addition, we consider a distributed version of the proposed topology control scheme. Our findings are substantiated by an extensive simulation study, through which we show that the shape Cooperative Bridges scheme substantially increases the connectivity with tolerable increase of transmission power compared to other existing topology control schemes, which means that it outperforms in terms of a connectivity-to-power ratio.

The prosperity of IEEE 802.11-based Wi-Fi networks aggravates cross-technology interference to IEEE 802.15.4-enabled ZigBee networks widely deployed to enable various Internet-of-Things applications. To make ZigBee communication reliable and robust even in a dense Wi-Fi environment, taming Wi-Fi interference in ZigBee networks especially from the perspective of physical layer is of paramount importance. In this context, this work takes aim to design a novel Wi-Fi interference-resilient ZigBee decoder called PolarScout, which separates collided ZigBee signal samples out of Wi-Fi interference to bootstrap ZigBee data decoding. Unlike several existing solutions which need clear signal preamble, tremendous signal strength difference between ZigBee and Wi-Fi, and Wi-Fi interference recognition in prior to ZigBee decoding, PolarScout aims at direct ZigBee decoding in a more generic and challenging case where Wi-Fi interference features a wide range of power levels and arises within a ZigBee packet at an arbitrary position. At the heart of PolarScout lies a subtle shell-shaping technique which harnesses a customized sample sequence to smooth the shell of corrupted signal samples. PolarScout then refers to the resulting shell to recover each contaminated ZigBee sample. Experimental results validate the superiority of PolarScout and its resilience to a wide range of Wi-Fi interference types.