Dynamic resiliency analysis of key predistribution in wireless sensor networks

Wireless sensor networks have been analyzed for more than a decade from operational and security points of view. Several key predistribution schemes have been proposed in the literature. Although valuable and state-of-the-art proposals have been made, their corresponding security analyses have not been performed by considering the dynamic nature of networking behavior and the time dimension. The sole metric used for resiliency analysis of key predistribution schemes is "fraction of links compromised" which is roughly defined as the ratio of secure communication links that the adversary can compromise over all secure links. However, this metric does not consider the dynamic nature of the network; it just analyzes a snapshot of the network without considering the time dimension. For example, possible dead nodes may cause change of routes and some captured links become useless for the attacker as time goes by. Moreover, an attacker cannot perform sensor node capturing at once, but performs over time. That is why a methodology for dynamic security analysis is needed in order to analyze the change of resiliency in time a more realistic way. In this paper, we propose such a dynamic approach to measure the resiliency of key predistribution schemes in sensor networks. We take the time dimension into account with a new performance metric, "captured message fraction". This metric is defined as the percentage of the messages generated within the network to be forwarded to the base station (sink) that are captured and read by the attacker. Our results show that for the cases where the static fraction of links compromised metric indicates approximately 40% of the links are compromised, our proposed captured message fraction metric shows 80% of the messages are captured by the attacker. This clearly proves the limitations of the static resiliency analysis in the literature

On the Deployment of Healthcare Applications over Fog Computing Infrastructure

Fog computing is considered as the most promising enhancement of the traditional cloud computing paradigm in order to handle potential issues introduced by the emerging Interned of Things (IoT) framework at the network edge. The heterogeneous nature, the extensive distribution and the hefty number of deployed IoT nodes will disrupt existing functional models, creating confusion. However, IoT will facilitate the rise of new applications, with automated healthcare monitoring platforms being amongst them. This paper presents the pillars of design for such applications, along with the evaluation of a working prototype that collects ECG traces from a tailor-made device and utilizes the patient's smartphone as a Fog gateway for securely sharing them to other authorized entities. This prototype will allow patients to share information to their physicians, monitor their health status independently and notify the authorities rapidly in emergency situations. Historical data will also be available for further analysis, towards identifying patterns that may improve medical diagnoses in the foreseeable future

Analyzing Delay in Wireless Multi-hop Heterogeneous Body Area Networks

With increase in ageing population, health care market keeps growing. There is a need for monitoring of health issues. Wireless Body Area Network (WBAN) consists of wireless sensors attached on or inside human body for monitoring vital health related problems e.g, Electro Cardiogram (ECG), Electro Encephalogram (EEG), ElectronyStagmography (ENG) etc. Due to life threatening situations, timely sending of data is essential. For data to reach health care center, there must be a proper way of sending data through reliable connection and with minimum delay. In this paper transmission delay of different paths, through which data is sent from sensor to health care center over heterogeneous multi-hop wireless channel is analyzed. Data of medical related diseases is sent through three different paths. In all three paths, data from sensors first reaches ZigBee, which is the common link in all three paths. Wireless Local Area Network (WLAN), Worldwide Interoperability for Microwave Access (WiMAX), Universal Mobile Telecommunication System (UMTS) are connected with ZigBee. Each network (WLAN, WiMAX, UMTS) is setup according to environmental conditions, suitability of device and availability of structure for that device. Data from these networks is sent to IP-Cloud, which is further connected to health care center. Delay of data reaching each device is calculated and represented graphically. Main aim of this paper is to calculate delay of each link in each path over multi-hop wireless channel.Comment: arXiv admin note: substantial text overlap with arXiv:1208.240

Secure and Reconfigurable Network Design for Critical Information Dissemination in the Internet of Battlefield Things (IoBT)

The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas such as smart homes, smart cities, health care, transportation, etc. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to the battlefield specific challenges such as the absence of communication infrastructure, and the susceptibility of devices to cyber and physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and the information dissemination in the presence of adversaries. This work aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to study the communication of mission-critical data among different types of network devices and consequently design the network in a cost effective manner.Comment: 8 pages, 9 figure