Incentive mechanism design for citizen reporting application using Stackelberg game

The growing utilization of smartphones equipped with various sensors to collect and analyze information around us highlights a paradigm called mobile crowdsensing. To motivate citizens’ participation in crowdsensing and compensate them for their resources, it is necessary to incentivize the participants for their sensing service. There are several studies that used the Stackelberg game to model the incentive mechanism, however, those studies did not include a budget constraint for limited budget case. Another challenge is to optimize crowdsourcer (government) profit in conducting crowdsensing under the limited budget then allocates the budget to several regional working units that are responsible for the specific city problems. We propose an incentive mechanism for mobile crowdsensing based on several identified incentive parameters using the Stackelberg game model and applied the MOOP (multi-objective optimization problem) to the incentive model in which the participant reputation is taken into account. The evaluation of the proposed incentive model is performed through simulations. The simulation indicated that the result appropriately corresponds to the theoretical properties of the model

Quality-of-information aware transmission policies with time-varying links

Abstract—We consider Quality-of-Information (QoI) aware transmission policies in the presence of time-varying links in a mobile ad hoc network. QoI, tailored for military tactical networks, is defined by a set of attributes relevant to the application. Time-varying nature of links in practical networks leads to uncertainty in evaluating QoI utility to be delivered to end users. This delivered-QoI utility is a function of both attributes provided by the source input, as a result of observing certain events, and the channel induced attributes that impact the QoI obtained at the destination. The goal of this paper is to attain the maximum QoI output utility, termed as Operational Information Content Capacity (OICC) of a network. First, for a single link, we demonstrate that the optimal decision structure for transmission is threshold-based. Next, we consider multihop relay networks. For the basic model of a two-hop relay network, we propose transmission scheduling and link activation schemes based on approximate dynamic programming methods. Furthermore, we exploit time-variations of links by opportunistic scheduling by employing buffers at the relay node. We demonstrate that significant gains in QoI output utility are gained by opportunistic scheduling algorithms. I

Distributed optimisation framework for in-network data processing

In an information network consisting of different types of communication devices equipped with various types of sensors, it is inevitable that a huge amount of data will be generated. Considering the practical network constraints such as bandwidth and energy limitations, storing, processing and transmitting this very large volume of data is very challenging, if not impossible. However, In-Network Processing (INP) has opened a new door to possible solutions for optimising the utilisation of network resources. INP methods primarily aim to aggregate (e.g., compression, fusion and averaging) data from different sources with the objective of reducing the data volume for further transfer, thus, reducing energy consumption, and increasing the network lifetime. However, processing data often results in an imprecise outcome such as irrelevancy, incompleteness, etc. Therefore, besides characterising the Quality of Information (QoI) in these systems, which is important, it is also crucial to consider the effect of further data processing on the measured QoI associated with each specific piece of information. Typically, the greater the degree of data aggregation, the higher the computation energy cost that is incurred. However, as the volume of data is reduced after aggregation, less energy is needed for subsequent data transmission and reception. Furthermore, aggregation of data can cause deterioration of QoI. Therefore, there is a trade-off among the QoI requirement and energy consumption by computation and communication. We define the optimal data reduction rate parameter as the degree to which data can be efficiently reduced while guaranteeing the required QoI for the end user. Using wireless sensor networks for illustration, we concentrate on designing a distributed framework to facilitate controlling of INP process at each node while satisfying the end user’s QoI requirements. We formulate the INP problem as a non-linear optimisation problem with the objective of minimising the total energy consumption through the network subject to a given QoI requirement for the end user. The proposed problem is intrinsically a non-convex, and, in general, hard to solve. Given the non-convexity and hardness of the problem, we propose a novel approach that can reduce the computation complexity of the problem. Specifically, we prove that under the assumption of uniform parameters’ settings, the complexity of the proposed problem can be reduced significantly, which may be feasible for each node with limited energy supply to carry out the problem computation. Moreover, we propose an optimal solution by transforming the original problem to an equivalent one. Using the theory of duality optimisation, we prove that under a set of reasonable cost and topology assumptions, the optimal solution can be efficiently, obtained despite the non-convexity of the problem. Furthermore, we propose an effective and efficient distributed, iterative algorithm that can converge to the optimal solution. We evaluate our proposed complexity reduction framework under different parameter settings, and show that the problem with N variables can be reduced to the problem with logN variables presenting a significant reduction in the complexity of the problem. The validity and performance of the proposed distributed optimisation framework has been evaluated through extensive simulation. We show that the proposed distributed algorithm can converge to the optimal solution very fast. The behaviour of the proposed framework has been examined under different parameters’ setting, and checked against the optimal solution obtained via an exhaustive search algorithm. The results show the quick and efficient convergences for the proposed algorithm.Open Acces

Cyber Security of Critical Infrastructures

Critical infrastructures are vital assets for public safety, economic welfare, and the national security of countries. The vulnerabilities of critical infrastructures have increased with the widespread use of information technologies. As Critical National Infrastructures are becoming more vulnerable to cyber-attacks, their protection becomes a significant issue for organizations as well as nations. The risks to continued operations, from failing to upgrade aging infrastructure or not meeting mandated regulatory regimes, are considered highly significant, given the demonstrable impact of such circumstances. Due to the rapid increase of sophisticated cyber threats targeting critical infrastructures with significant destructive effects, the cybersecurity of critical infrastructures has become an agenda item for academics, practitioners, and policy makers. A holistic view which covers technical, policy, human, and behavioural aspects is essential to handle cyber security of critical infrastructures effectively. Moreover, the ability to attribute crimes to criminals is a vital element of avoiding impunity in cyberspace. In this book, both research and practical aspects of cyber security considerations in critical infrastructures are presented. Aligned with the interdisciplinary nature of cyber security, authors from academia, government, and industry have contributed 13 chapters. The issues that are discussed and analysed include cybersecurity training, maturity assessment frameworks, malware analysis techniques, ransomware attacks, security solutions for industrial control systems, and privacy preservation methods