110 research outputs found

    A Multiple Mobility Support Approach (MMSA) Based on PEAS for NCW in Wireless Sensor Networks

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    Wireless Sensor Networks (WSNs) can be implemented as one of sensor systems in Network Centric Warfare (NCW). Mobility support and energy efficiency are key concerns for this application, due to multiple mobile users and stimuli in real combat field. However, mobility support approaches that can be adopted in this circumstance are rare. This paper proposes Multiple Mobility Support Approach (MMSA) based on Probing Environment and Adaptive Sleeping (PEAS) to support the simultaneous mobility of both multiple users and stimuli by sharing the information of stimuli in WSNs. Simulations using Qualnet are conducted, showing that MMSA can support multiple mobile users and stimuli with good energy efficiency. It is expected that the proposed MMSA can be applied to real combat field

    Toward Sensor-Based Random Number Generation for Mobile and IoT Devices

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    The importance of random number generators (RNGs) to various computing applications is well understood. To ensure a quality level of output, high-entropy sources should be utilized as input. However, the algorithms used have not yet fully evolved to utilize newer technology. Even the Android pseudo RNG (APRNG) merely builds atop the Linux RNG to produce random numbers. This paper presents an exploratory study into methods of generating random numbers on sensor-equipped mobile and Internet of Things devices. We first perform a data collection study across 37 Android devices to determine two things-how much random data is consumed by modern devices, and which sensors are capable of producing sufficiently random data. We use the results of our analysis to create an experimental framework called SensoRNG, which serves as a prototype to test the efficacy of a sensor-based RNG. SensoRNG employs collection of data from on-board sensors and combines them via a lightweight mixing algorithm to produce random numbers. We evaluate SensoRNG with the National Institute of Standards and Technology statistical testing suite and demonstrate that a sensor-based RNG can provide high quality random numbers with only little additional overhead

    Honware: A Virtual Honeypot Framework for Capturing CPE and IoT Zero Days

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    Existing solutions are ineffective in detecting zero day exploits targeting Customer Premise Equipment (CPE) and Internet of Things (IoT) devices. We present honware, a high-interaction honeypot framework which can emulate a wide range of devices without any access to the manufacturers' hardware. Honware automatically processes a standard firmware image (as is commonly provided for updates), customises the filesystem and runs the system with a special pre-built Linux kernel. It then logs attacker traffic and records which of their actions led to a compromise. We provide an extensive evaluation and show that our framework improves upon existing emulation strategies which are limited in their scalability, and that it is significantly better both in providing network functionality and in emulating the devices' firmware applications - a crucial aspect as vulnerabilities are frequently exploited by attackers in front-end functionalities such as web interfaces. Honware's design precludes most honeypot fingerprinting attacks, and as its performance is comparable to that of real devices, fingerprinting with timing attacks can be made far from trivial. We provide four case studies in which we demonstrate that honware is capable of rapid deployment to capture the exact details of attacks along with malware samples. In particular we identified a previously unknown attack in which the default DNS for an ipTIME N604R wireless router was changed. We believe that honware is a major contribution towards re-balancing the economics of attackers and defenders by reducing the period in which attackers can exploit zero days at Internet scale

    Energy Management in LTE Networks

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    Wireless cellular networks have seen dramatic growth in number of mobile users. As a result, data requirements, and hence the base-station power consumption has increased significantly. It in turn adds to the operational expenditures and also causes global warming. The base station power consumption in long-term evolution (LTE) has, therefore, become a major challenge for vendors to stay green and profitable in competitive cellular industry. It necessitates novel methods to devise energy efficient communication in LTE. Importance of the topic has attracted huge research interests worldwide. Energy saving (ES) approaches proposed in the literature can be broadly classified in categories of energy efficient resource allocation, load balancing, carrier aggregation, and bandwidth expansion. Each of these methods has its own pros and cons leading to a tradeoff between ES and other performance metrics resulting into open research questions. This paper discusses various ES techniques for the LTE systems and critically analyses their usability through a comprehensive comparative study

    Measurement and Analysis of the Swarm Social Network With Tens of Millions of Nodes

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    Social graphs have been widely used for representing the relationship among users in online social networks (OSNs). As crawling an entire OSN is resource-and time-consuming, most of the existing works only pick a sampled subgraph for study. However, this may introduce serious inaccuracy into the analytic results, not to mention that some important metrics cannot even be calculated. In this paper, we crawl the entire social network of Swarm, a leading mobile social app with more than 60 million users, using a distributed approach. Based on the crawled massive user data, we conduct a data-driven study to get a comprehensive picture of the whole Swarm social network. This paper provides a deep analysis of social interactions between Swarm users, and reveals the relationship between social connectivity and check-in activities.Peer reviewe
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