323 research outputs found
Inferring Person-to-person Proximity Using WiFi Signals
Today's societies are enveloped in an ever-growing telecommunication
infrastructure. This infrastructure offers important opportunities for sensing
and recording a multitude of human behaviors. Human mobility patterns are a
prominent example of such a behavior which has been studied based on cell phone
towers, Bluetooth beacons, and WiFi networks as proxies for location. However,
while mobility is an important aspect of human behavior, understanding complex
social systems requires studying not only the movement of individuals, but also
their interactions. Sensing social interactions on a large scale is a technical
challenge and many commonly used approaches---including RFID badges or
Bluetooth scanning---offer only limited scalability. Here we show that it is
possible, in a scalable and robust way, to accurately infer person-to-person
physical proximity from the lists of WiFi access points measured by smartphones
carried by the two individuals. Based on a longitudinal dataset of
approximately 800 participants with ground-truth interactions collected over a
year, we show that our model performs better than the current state-of-the-art.
Our results demonstrate the value of WiFi signals in social sensing as well as
potential threats to privacy that they imply
Smartphone malware based on synchronisation vulnerabilities
Smartphones are mobile phones that offer processing power and features like personal computers (PC) with the aim of improving user productivity as they allow users to access and manipulate data over networks and Internet, through various mobile applications. However, with such anywhere and anytime functionality, new security threats and risks of sensitive and personal data are envisaged to evolve. With the emergence of open mobile platforms that enable mobile users to install applications on their own, it opens up new avenues for propagating malware among various mobile users very quickly. In particular, they become crossover targets of PC malware through the synchronization function between smartphones and computers. Literature lacks detailed analysis of smartphones malware and synchronization vulnerabilities. This paper addresses these gaps in literature, by first identifying the similarities and differences between smartphone malware and PC malware, and then by investigating how hackers exploit synchronization vulnerabilities to launch their attacks
Profiling Performance of Application Partitioning for Wearable Devices in Mobile Cloud and Fog Computing
Wearable devices have become essential in our daily activities. Due to battery constrains the use of computing, communication, and storage resources is limited. Mobile Cloud Computing (MCC) and the recently emerged Fog Computing (FC) paradigms unleash unprecedented opportunities to augment capabilities of wearables devices. Partitioning mobile applications and offloading computationally heavy tasks for execution to the cloud or edge of the network is the key. Offloading prolongs lifetime of the batteries and allows wearable devices to gain access to the rich and powerful set of computing and storage resources of the cloud/edge. In this paper, we experimentally evaluate and discuss rationale of application partitioning for MCC and FC. To experiment, we develop an Android-based application and benchmark energy and execution time performance of multiple partitioning scenarios. The results unveil architectural trade-offs that exist between the paradigms and devise guidelines for proper power management of service-centric Internet of Things (IoT) applications
Evaluating energy consumption on low-end smartphones
The relationship between battery consumption in
smartphones and the usage statistics of a phone is direct. Modern
smartphones, even low-end, are equipped with multiple wireless
technologies, e.g. GSM, 3G, WiFi and Bluetooth. Each of these
technologies has a different energy consumption profile. A
wireless mesh project in the Mankosi community in rural South
Africa is about to introduce low-end smartphones onto the
network. The mesh network is powered with solar-charged
batteries because the community at present does not have
electricity. Local residents also use these batteries to recharge cell
phones at a nominal cost. Introduction of smartphones will
increase the recharge frequency as phone usage will increase;
thus draining a phone battery more quickly, as well as escalate
recharge costs. Thus, the smartphones must be chosen and used
effectively in order for batteries to last longer. Related work
identifies WiFi wireless technology as the most battery efficient
way of transfer when compared to GSM, 3G and Bluetooth. This
research proposes experiments to further investigate energy
efficiency of WiFi in low-end smartphones that we intend to use
for local and breakout voice over Internet protocol (VoIP) calls
and data services, on a rural wireless mesh network
A survey on wireless body area networks for eHealthcare systems in residential environments
The progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to the base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments
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