WSN operability during persistent attack execution

Wireless Sensor Networks (WSNs) are utilized in a number of critical infrastructures, e.g. healthcare, disaster and relief. In sensitive environments, it is vital to maintain the operability of the network in an effort to support the decision-making process that depends on the sensors’ observations. The network’s operability can be maintained if observations can reach the specified destination and also if the sensors have adequate energy resources. The operability is negatively affected by security attacks, such as the selective forward and the denial of service (DoS), that can be executed against the WSN. The attacks’ impact greatly depends on the attackers’ capabilities such as their knowledge and the number of malicious nodes they hold. Currently, the research community focuses on addressing casual attackers that don’t persist with their attack strategy. However, the proposed solutions cannot address persistent attackers that continue with their attack execution after the network has applied appropriate recovery countermeasures. Designing an adaptive recovery strategy is challenging as a number of issues need to be taken into consideration such as the network’s density, the number of malicious nodes and the persistent attack strategy. This research work formulates a persistent attack strategy and investigates the integration of different recovery countermeasures in WSNs. The evaluation results demonstrate that an adaptive recovery strategy can enhance the network’s recovery benefits, in terms of increased packet delivery and decreased energy consumption, and prolong its operability. Moreover, the observations made are envisioned to encourage new contributions in the area of adaptive intrusion recovery in WSNs

Received Signal Strength for Randomly Distributed Molecular Nanonodes

We consider nanonodes randomly distributed in a circular area and characterize the received signal strength when a pair of these nodes employ molecular communication. Two communication methods are investigated, namely free diffusion and diffusion with drift. Since the nodes are randomly distributed, the distance between them can be represented as a random variable, which results in a stochastic process representation of the received signal strength. We derive the probability density function of this process for both molecular communication methods. Specifically for the case of free diffusion we also derive the cumulative distribution function, which can be used to derive transmission success probabilities. The presented work constitutes a first step towards the characterization of the signal to noise ratio in the considered setting for a number of molecular communication methods.Comment: 6 pages, 6 figures, Nanocom 2017 conferenc

Security And Privacy Of Medical Data:Challenges For Next-Generation Patient-Centric Healthcare Systems

This work has been supported by the EU H2020 grant Serums: Securing Medical Data in Smart Patient-Centric Healthcare Systems (code 826278).We describe the recently-started EU H2020 Serums: Securing Medical Data in Smart Patient-Centric Healthcare Systems project that aims to develop novel techniques for safe and secure collection, storage, exchange and analysis of medical data, allowing the patients of the next-generation smart healthcare centers to get the best possible treatment while respecting privacy and ownership of their sensitive personal data. Our goal is to significantly enhance trust in the new medical systems. We outline the techniques that will be extended/developed over the course of the project and describe the use cases that will be used to verify the effectiveness of these technologies in practice.Postprin

Distributed Artificial Intelligence Solution for D2D Communication in 5G Networks

Device to Device (D2D) Communication is one of the technology components of the evolving 5G architecture, as it promises improvements in energy efficiency, spectral efficiency, overall system capacity, and higher data rates. The above noted improvements in network performance spearheaded a vast amount of research in D2D, which have identified significant challenges that need to be addressed before realizing their full potential in emerging 5G Networks. Towards this end, this paper proposes the use of a distributed intelligent approach to control the generation of D2D networks. More precisely, the proposed approach uses Belief-Desire-Intention (BDI) intelligent agents with extended capabilities (BDIx) to manage each D2D node independently and autonomously, without the help of the Base Station. The paper includes detailed algorithmic description for the decision of transmission mode, which maximizes the data rate, minimizes the power consumptions, while taking into consideration the computational load. Simulations show the applicability of BDI agents in jointly solving D2D challenges.Comment: 10 pages,9 figure