Uneven key pre-distribution scheme for multi-phase wireless sensor networks

In multi-phase Wireless Sensor Networks (WSNs), sensor nodes are redeployed periodically to replace nodes whose batteries are depleted. In order to keep the network resilient against node capture attacks across different deployment epochs, called generations, it is necessary to refresh the key pools from which cryptographic keys are distributed. In this paper, we propose Uneven Key Pre-distribution (UKP) scheme that uses multiple different key pools at each generation. Our UKP scheme provides self healing that improves the resiliency of the network at a higher level as compared to an existing scheme in the literature. Moreover, our scheme provides perfect local and global connectivity. We conduct our simulations in mobile environment to see how our scheme performs under more realistic scenarios

A cell outage management framework for dense heterogeneous networks

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

What works? A review of actions addressing the social and economic determinants of Indigenous health

Introduction: The purpose of this paper is to review evidence relating to ‘what works’ to influence the social and economic determinants of Indigenous health, in order to reduce health inequities, and ultimately contribute to closing the life expectancy gap between Indigenous and non-Indigenous Australians. We outline a conceptual framework for understanding how social and economic determinants influence health and wellbeing, and identify a number of key determinants of health. We review evidence relating to how each determinant is associated with Indigenous health and wellbeing, and then consider specific actions designed to improve Indigenous outcomes in each of these areas in order to determine the characteristics of successful initiatives. Based on our conceptual framework, we link successful actions which result in positive outcomes for Indigenous Australians in each of the key determinants to ultimately improving health and wellbeing and contributing towards ‘closing the gap’ in health and wellbeing. We note that many actions we consider only aim to improve the situation for Indigenous Australians in regard to that specific area (for example, education, housing) and were not devised to take direct action to improve health, even though the evidence indicates that those actions may be likely to contribute to improved health over the longer term

Efficient threshold self-healing key distribution with sponsorization for infrastructureless wireless networks

Self-healing key distribution schemes are particularly useful when there is no network infrastructure or such infrastructure has been destroyed. A self-healing mechanism can allow group users to recover lost session keys and is therefore quite suitable for establishing group keys over an unreliable network, especially for infrastructureless wireless networks, where broadcast messages loss may occur frequently. An efficient threshold self-healing key distribution scheme with favorable properties is proposed in this paper. The distance between two broadcasts used to recover the lost one is alterable according to network conditions. This alterable property can be used to shorten the length of the broadcast messages. The second property is that any more than threshold-value users can sponsor a new user to join the group for the subsequent sessions without any interaction with the group manager. Furthermore, the storage overhead of the self-healing key distribution at each group user is a polynomial over a finite field, which will not increase with the number of sessions. In addition, if a smaller group of users up to a threshold-value were revoked, the personal keys for non-revoked users can be reused

An efficient self-healing key distribution scheme

Self-healing key distribution schemes enable a group user to recover session keys from two broadcast messages he received before and after those sessions, even if the broadcast messages for the middle sessions are lost due to network failure. These schemes are quite suitable in supporting secure communication over unreliable networks such as sensor networks and ad hoc networks. An efficient self-healing key distribution scheme is proposed in this paper. The scheme bases on the concept of access polynomial and self-healing key distribution model constructed by Hong et al. The new scheme reduces communication and computation overheads greatly yet still keeps the constant storageoverhead

Trade-Off between Collusion Resistance and User Life Cycle in Self-Healing Key Distributions with t-Revocation

We solve the problem of resisting the collusion attack in the one-way hash chain based self-healing key distributions introduced by Dutta et al., coupling it with the prearranged life cycle based approach of Tian et al. that uses the same self-healing mechanism introduced in Dutta et al. Highly efficient schemes are developed compared to the existing works with the trade-off in pre-arranged life cycles on users by the group manager and a slight increase in the storage overhead. For scalability of business it is often necessary to design more innovation and flexible business strategies in certain business models that allow contractual subscription or rental, such as subscription of mobile connection or TV channel for a pre-defined period. The subscribers are not allowed to revoke before their contract periods (life cycles) are over. Our schemes fit into such business environment. The proposed schemes are proven to be computationally secure and resist collusion between new joined users and revoked users together with forward and backward secrecy. The security proof is in an appropriate security model. Moreover, our schemes do not forbid revoked users from rejoining in later sessions unlike the existing self- healing key distribution schemes