Sybil attacks are a challenging security threat in ad-hoc and vehicular networks. A malicious node can successfully launch a Sybil attack with high probability. However, the targeted node has difficulty in discriminating whether the neighboring node is a Sybil or an honest node. Furthermore, Sybil attacks may have the grave consequence of compromising network reliability. To this end, we propose a novel method to effectively detect and thwart Sybil attacks through electro-acoustic positioning with context-aware information. By using electro-acoustic positioning, each node accurately verifies nearby Sybil identities, and shares the verified node information with others. As a result, our proposed scheme allows small pieces of information to form a complete picture of the Sybil node distribution. Our extensive simulation results show that our approach outperforms existing solutions in detection of Sybil attacks and provides better accuracy even in relatively sparse networks.

Recently, the simultaneous usage of various wireless technologies, such as Wi-Fi, Bluetooth, ZigBee and so on, becomes common and the propensity tends to be more complicated with the prevalence of smart and IoT devices. Such technology increments, however, intensify challenging Cross-Technology Interference (CTI) issues in a shared wireless band. To circumvent the hurdle, we propose a way which minimizes the medium occupation of wireless devices, called GreenAir. In the range of preserving network performances like connectivity and delay, GreenAir supports to generate the lesser communication links among homogeneous devices by utilizing a duty-cycle control. We experiment whether such approach is feasible to reduce CTI under the mixture of heterogeneous devices. Through retransmission and throughput measures in the combined use of Wi-Fi, ZigBee and Microwave oven (aggressive offender in ISM band), we validate that there exist large margins to reduce CTI via OPNET modeler. For the better applicability in IoT surroundings where the addition/removal of devices is arbitrary and frequent, we further tailor the operation of GreenAir to be distributed and autonomous. As our approach only requires local communications among homogeneous devices, the usability of GreenAir is device-agnostic.

Dense Wi-Fi and Bluetooth (BT) environments become increasingly common so that the coexistence issue between Wi-Fi and BT is imperative to solve. In this paper, we propose BlueCoDE, a coordination scheme for multiple neighboring BT piconets, to make them collision-free and less harmful to Wi-Fi. BlueCoDE reuses BT's existing PHY and MAC design, thus making it practically feasible. We implement a prototype of BlueCoDE on Ubertooth One platform and corroborate the performance gain via analysis, NS-3 simulations, and prototype-based experiments. Our experimental results show that with merely 10 legacy BT piconets, neighboring Wi-Fi network becomes useless achieving under 1 Mb/s throughput, while BlueCoDE enables the Wi-Fi throughput always remain above 12 Mb/s. We expect BlueCoDE to be a breakthrough solution for coexistence in dense Wi-Fi and BT environments.

This paper deals with the problem of severe wireless performance degradation when multiple wireless technologies are concurrently utilized in a same user device. This type of usage is already frequent in most smartphones and laptops, such as streaming Bluetooth audio while using a Wi-Fi download, and is more intensifying with IoT device deployment which triggers the coexistence of heterogeneous wireless technologies. To lower the form factor and the cost, chip vendors package multiple wireless interfaces into a single combo-chip where a common antenna is shared by multiple network technologies in a time division multiplexing manner. We issue that the careless operations of combo-chip design incur indeed performance degradation for in-device wireless coexistence and show the experimental results via TCP performance measurements in several smartphones and laptops. Our analysis reveals that the behavior negatively affects not only on the transmit power management of wireless access point, but also on the congestion control of TCP sender. We propose a cooperative switching scheme which incorporates TCP control behaviors for better coexistence and implement it on Android and Linux devices. Under the simultaneous use of in-device network interfaces, our approach led a WLAN throughput increment up to eight times without the mentioned issues. Further, this does not require any modification of TCP sender and wireless access point. Thus, the approach is directly applicable to existing mobile devices and also easily extendable to the combination of other in-device wireless technologies.

The concurrent deployment of heterogeneous wireless networks such as Wi-Fi, Bluetooth, and ZigBee has led to the severe interference problems in the 2.4GHz ISM band. In particular, ZigBee networks are susceptible to the interferences from other wireless technologies; For example, strong Wi-Fi signals trigger false alarms to ZigBee device that is performing low power idle listening and cause appreciable energy waste. In this paper, we propose a novel ZigBee signal detection scheme, called CoSense that accurately identifies ZigBee signals in the presence of the cross-technology interferences. CoSense, which is a highly reliable signal correlation technique, enjoys the following three advantages: First, CoSense reduces false wake-ups, which typically consume energy unnecessarily. Secondly, CoSense is robust against heterogeneous interference scenarios because its signal correlation feature has been shown to work well in bad channel conditions. Third, CoSense is backward-compatible and does not require to change the traditional ZigBee networks. We have implemented CoSense on the USRP/GNURadio platform in order to prove its feasibility. The results show that, under typical setting, CoSense indeed reduces the false alarm rate and its overhead is tolerable. We can conclude that CoSense saves energy by up to 63% in heterogeneous network environments where weak ZigBee signals are overwhelmed by strong signals such as Wi-Fi.

Recently, we have received a growing number of reports that complain about poor and unstable internet connections at bus stops in metro Seoul. Careful analyses led us to conclude that Wi-Fi APs equipped on buses instigate the trouble. According to the ambitious free Wi-Fi expansion plan by the city of Seoul, public buses started to equip Wi-Fi APs. As buses with APs stop and go, they actualize intermittent connection opportunities to riders waiting at the bus stops. However, the connection durations are too short such that bus APs are a nuisance rather than a convenience. We collected the basic statistics such as AP inter-arrival and sojourn times and measured link level performance metrics. We observed the effect of frequent frame losses on the TCP congestion control and eventually on the TCP throughput. We also measured the performance of applications such as PLT (Page Load Time). The measurement results showed that passing APs are useful only for some applications in very limited situations while they are virtually useless and just irritations in many cases. We also discovered that poor Wi-Fi connections pervert MPTCP; MPTCP performs worse than the generic single path TCP over the LTE network. We expect that our results will be used as the reference data in redesigning Wi-Fi offloading mechanisms as well as in planning and deploying urban Wi-Fi networks.

Although using high-quality streaming services becomes more common on mobile devices, the increment of streaming quality, which generally requires the larger playback bandwidth, frequently incurs undesirable delays or jitters that hamper user experiences. Recent mobile devices usually equip multiple network interfaces and their concurrent utilization can be beneficial to retain the necessary bandwidth. This, however, requires the more energy consumption of mobile devices whose resource is limited due to battery-based operations. For this reason, streaming performance and energy consumption should not be independently considered. Under multiple network interface utilization, we investigate the characteristics of each component, based on two-level convex optimization, and propose a solution to optimize them together. The algorithm is tested on Android phone where LTE and Wi-Fi networks are concurrently enabled. Compared to LTE only method, it shows that the proposed approach can obtain 1.8 times good-put increment only with the half amount of energy consumption. Our approach can be utilized for almost all mobile devices without server-side modifications.

Modern mobile devices are equipped with multiple network interfaces, including 3G/LTE and WiFi. Bandwidth aggregation over LTE and WiFi links offers an attractive opportunity of supporting bandwidth-intensive services, such as high-quality video streaming, on mobile devices. However, achieving effective bandwidth aggregation in mobile environments raises several challenges related to deployment, link heterogeneity, network fluctuation, and energy consumption. We present GreenBag, an energy-efficient bandwidth aggregation middleware that supports real-time data-streaming services over asymmetric wireless links, requiring no modifications to the existing Internet infrastructure and servers. GreenBag employs several techniques, including medium load balancing, efficient segment management, and energy-aware mode control, to resolve such challenges. We implement a prototype of GreenBag on Android-based mobile devices which hosts, to the best knowledge of the authors, the first LTE-enabled bandwidth aggregation prototype for energy-efficient real-time video streaming. Our experiment results in both emulated and real-world environments show that GreenBag not only achieves good bandwidth aggregation to provide QoS in bandwidth-scarce environments but also efficiently saves energy on mobile devices. Moreover, energy-aware GreenBag can minimize video interruption while consuming 14-25% less energy than the non-energy-aware counterpart in real-world experiments.

Due to replacement infeasibility, methods to extend the life-time of sensors have been an issue in Wireless Sensor Networks (WSNs) and these should consider network connectivity simultaneously. Controlling the sleep/awake of sensors is one simple way to reduce their energy consumption. However, this causes a network connectivity degradation by varying network connection topology. For this reason, we propose a simple and autonomous sensor sleep/awake method to achieve their balance. The size of clusters can be a metric to measure network connectivity in that a path exists among any cluster node. From percolation theory, we observe that a cluster size suffers a sharp transition based on edge connection patterns. This allows us to design a sensor sleep/awake algorithm which has an immense simplicity, but still requires global topology information. In many cases, sensors are not aware of the global topology. Further, managing the information becomes challenging under physical topology changes such as sensor add/drop. We show that the global knowledge requirement can be resolved by using a consensus algorithm. Through several graph tests, we show that our method achieves a network balancing between connectivity and life-time with preserving its simplicity. Also, the balancing is autonomous even under physical topology variations.

We propose methods to estimate centrality in Smart-Grid Networks (SGNs) from the view of a flow-based approach. In several network categories, centrality metrics, such as degree, closeness and betweenness, have provided ways to investigate the importance or weakness of components. These well-known metrics utilize either non-global or shortest-path information. We mention several observations which try to use these metrics into a vulnerability measure of SGNs. For this, we stress that using a proper metric, which captures the core network characteristic, is important to induce a correct network analysis. This proper metric changes with network categories. In contrast to data networks, SGNs possess a fundamentally different property that comes from electricity distributions and this requires us to include a multi-path consideration. About this issue, we explain the feasibility of flow-based analysis and suggest to utilize an effective resistance as a distance measure. This allows us to propose new centrality metrics utilizable in SGNs. In several power grid test-beds, our metrics are tested and the differences from using current centrality metrics are compared. These results indicate that SGNs are more scale-free than the estimation from currently used metrics and provide the reason for cascading failure phenomena observed in SGNs. Additionally, we show that the multi-path effect becomes more severe with a network size increment.