Digital Investigation of Security Attacks on Cardiac Implantable Medical Devices

A Cardiac Implantable Medical device (IMD) is a device, which is surgically implanted into a patient's body, and wirelessly configured using an external programmer by prescribing physicians and doctors. A set of lethal attacks targeting these devices can be conducted due to the use of vulnerable wireless communication and security protocols, and the lack of security protection mechanisms deployed on IMDs. In this paper, we propose a system for postmortem analysis of lethal attack scenarios targeting cardiac IMDs. Such a system reconciles in the same framework conclusions derived by technical investigators and deductions generated by pathologists. An inference system integrating a library of medical rules is used to automatically infer potential medical scenarios that could have led to the death of a patient. A Model Checking based formal technique allowing the reconstruction of potential technical attack scenarios on the IMD, starting from the collected evidence, is also proposed. A correlation between the results obtained by the two techniques allows to prove whether a potential attack scenario is the source of the patient's death.Comment: In Proceedings AIDP 2014, arXiv:1410.322

Service Provisioning for Lightweight Community Cloud Infrastructures

Service provision in low power network environments remains a very difficult task due to the lightweight nature of the devices involved. This paper explores and demonstrates the efficacy of the light weight resource allocation protocol (LRAP) in allocating services in a heterogeneous network environment consisting of primarily low power nodes. We show that with the correct resource brokering approach, services can be provisioned on nodes in a way which does not cripple individual nodes in network, while ensuring that services are distributed to nodes in the network where the lowest impact on overall performance will be made. This strategy provides an effective way of providing services especially in low power environment

Autonomous pipeline monitoring and maintenance system: a RFID-based approach

Pipeline networks are one of the key infrastructures of our modern life. Proactive monitoring and frequent inspection of pipeline networks are very important for sustaining their safe and efficient functionalities. Existing monitoring and maintenance approaches are costly and inefficient because pipelines can be installed in large scale and in an inaccessible and hazardous environment. To overcome these challenges, we propose a novel Radio Frequency IDentification (RFID)-based Autonomous Maintenance system for Pipelines, called RAMP, which combines robotic, sensing, and RFID technologies for efficient and accurate inspection, corrective reparation, and precise geo-location information. RAMP can provide not only economical and scalable remedy but also safe and customizable solution. RAMP also allows proactive and corrective monitoring and maintenance of pipelines. One prominent advantage of RAMP is that it can be applied to a large variety of pipeline systems including water, sewer, and gas pipelines. Simulation results demonstrate the feasibility and superior performance of RAMP in comparison to the existing pipeline monitoring systems

Trajectory planing for cooperating unmanned aerial vehicles in the IoT

The use of Unmanned Aerial Vehicles (UAVs) in data transport has attracted a lot of attention and applications, as a modern traffic engineering technique used in data sensing, transport, and delivery to where infrastructure is available for its interpretation. Due to UAVs’ constraints such as limited power lifetime, it has been necessary to assist them with ground sensors to gather local data, which has to be transferred to UAVs upon visiting the sensors. The management of such ground sensor communication together with a team of flying UAVs constitutes an interesting data muling problem, which still deserves to be addressed and investigated. This paper revisits the issue of traffic engineering in Internet-of-Things (IoT) settings, to assess the relevance of using UAVs for the persistent collection of sensor readings from the sensor nodes located in an environment and their delivery to base stations where further processing is performed. We propose a persistent path planning and UAV allocation model, where a team of heterogeneous UAVs coming from various base stations are used to collect data from ground sensors and deliver the collected information to their closest base stations. This problem is mathematically formalised as a real-time constrained optimisation model, and proven to be NP-hard. The paper proposes a heuristic solution to the problem and evaluates its relative efficiency through performing experiments on both artificial and real sensors networks, using various scenarios of UAVs settings

Coverage Assessment and Target Tracking in 3D Domains

Recent advances in integrated electronic devices motivated the use of Wireless Sensor Networks (WSNs) in many applications including domain surveillance and mobile target tracking, where a number of sensors are scattered within a sensitive region to detect the presence of intruders and forward related events to some analysis center(s). Obviously, sensor deployment should guarantee an optimal event detection rate and should reduce coverage holes. Most of the coverage control approaches proposed in the literature deal with two-dimensional zones and do not develop strategies to handle coverage in three-dimensional domains, which is becoming a requirement for many applications including water monitoring, indoor surveillance, and projectile tracking. This paper proposes efficient techniques to detect coverage holes in a 3D domain using a finite set of sensors, repair the holes, and track hostile targets. To this end, we use the concepts of Voronoi tessellation, Vietoris complex, and retract by deformation. We show in particular that, through a set of iterative transformations of the Vietoris complex corresponding to the deployed sensors, the number of coverage holes can be computed with a low complexity. Mobility strategies are also proposed to repair holes by moving appropriately sensors towards the uncovered zones. The tracking objective is to set a non-uniform WSN coverage within the monitored domain to allow detecting the target(s) by the set of sensors. We show, in particular, how the proposed algorithms adapt to cope with obstacles. Simulation experiments are carried out to analyze the efficiency of the proposed models. To our knowledge, repairing and tracking is addressed for the first time in 3D spaces with different sensor coverage schemes

Border surveillance monitoring using Quadcopter UAV-Aided Wireless Sensor Networks

In this paper we propose a novel cooperative bordersurveillance solution, composed of a Wireless Sensor Network (WSN) deployed terrestrially to detect and track trespassers, and a set of lightweight unmanned aircraft vehicles (UAVs) in the form of quadcopters that interact with the deployed WSN to improve the border surveillance, the detection and investigation of network failures, the maintenance of the sensor network, the tracking of trespasser, the capture and transmission of realtime video of the intrusion scene, and the response to hostage situations. A heuristic-based scheduling algorithm is described to optimize the tracking mission by increasing the rate of detected trespassers spotted by the quadcopters. Together with the design of the electrical, mechanical and software architecture of the proposed VTail quadcopter, we develop in this paper powerless techniques to accurately localize terrestrial sensors using RFID technology, compute the optimal positions of the new sensors to drop, relay data between isolated islands of nodes, and wake up sensors to track intruders. The developed VTail prototype is tested to provide valid and accurate parameters’ values to the simulation. The latter is conducted to evaluate the performance of the proposed WSN-based surveillance solution