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The rapid expansion of wireless information technology (IT) coupled with a dramatic increase in security breaches forces organizations to develop comprehensive strategies for managing security risks. The problem addressed was the identification of security risk management practices and human errors of IT administrators, putting the organization at risk for external security intrusion. The purpose of this non-experimental quantitative study was to investigate and determine the security risk assessment practices used by IT administrators to protect the confidentiality and integrity of the organization's information. The research questions focused on whether the security risk management practices of IT administrators met or exceeded the minimally accepted practices and standards for wireless networking. The security risk assessment and management model established the theoretical framework. The sample was 114 participants from small to medium IT organizations comprised of security engineers, managers, and end users. Data collection was via an online survey. Data analysis included both descriptive and inferential statistical methods. The results revealed that greater than 80% of participants conducted appropriate risk management and review assessments. This study underscored the need for a more comprehensive approach to managing IT security risks. IT managers can use the outcome of this study as a benchmark for evaluating their current risk assessment procedures. Experiencing security breaches in organizations may be inevitable. However, when organizations and industry leaders can greatly reduce the cost of a data breach by developing effective risk management plans that lead to better security outcomes, positive social change can be realized.

In recent years, mobile computing has become an integral part of society. As the cost of laptops and wireless networking hardware has declined, society has become increasingly connected. High speed wireless internet access is increasingly becoming part of our daily lives. As a result of this dependence on instant access to information, there is a growing need to create wireless networks without having access to a fixed networking infrastructure. Instead of relying in fixed infrastructure, these mobile nodes can be joined to create an ad hoc network to facilitate information sharing. The ad hoc nature of these networks requires different protocols than traditional networks. This research is motivated by the observation that radio communications are greatly affected by the physical environment. In hilly or urban environments, the performance of a wireless network is much lower than in large open areas. However, MANET protocols typically consider the physical environment only when it causes a change in connectivity. We examine whether the network can estimate the physical environment and predict its impact on the network, rather than waiting to react to the physical environment. This research demonstrates the feasibility of using terrain and location information to improve routing in mobile ad hoc networks through the development of a distributed routing algorithm that uses location and digital terrain information to efficiently deliver packets in a mobile ad hoc network. Through a comprehensive set of simulations, we show that the new algorithm performs better than current MANET protocols in terms of standard metrics: delay, throughput, packet loss, and efficiency.

<p>It is predicted that there would be 41.6 billion IoT devices by 2025, which has kindled new interests on the timing coordination between sensors and controllers, i.e., how to use the waiting time to improve the performance. Sun et al. showed that a <i>controller</i> can strictly improve the data freshness, the so-called Age-of-Information (AoI), via careful scheduling designs. The optimal waiting policy for the <i>sensor</i> side was later characterized in the context of remote estimation. The first part of this work develops the jointly optimal sensor/controller waiting policy. It generalizes the above two important results in that not only do we consider joint sensor/controller designs, but we also assume random delay in both the forward and feedback directions. </p> <p> </p> <p>The second part of the work revisits and significantly strengthens the seminal results of Sun et al on the following fronts: (i) When designing the optimal offline schemes with full knowledge of the delay distributions, a new <i>fixed-point-based</i> method is proposed with <i>quadratic convergence rate</i>; (ii) When the distributional knowledge is unavailable, two new low-complexity online algorithms are proposed, which provably attain the optimal average AoI penalty; and (iii) the online schemes also admit a modular architecture, which allows the designer to <i>upgrade</i> certain components to handle additional practical challenges. Two such upgrades are proposed: (iii.1) the AoI penalty function incurred at the destination is unknown to the source node and must also be estimated on the fly, and (iii.2) the unknown delay distribution is Markovian instead of i.i.d. </p> <p> </p> <p>With the exponential growth of interconnected IoT devices and the increasing risk of excessive resource consumption in mind, the third part of this work derives an optimal joint cost-and-AoI minimization solution for multiple coexisting source-destination (S-D) pairs. The results admit a new <i>AoI-market-price</i>-based interpretation and are applicable to the setting of (i) general heterogeneous AoI penalty functions and Markov delay distributions for each S-D pair, and (ii) a general network cost function of aggregate throughput of all S-D pairs. </p> <p> </p> <p>In each part of this work, extensive simulation is used to demonstrate the superior performance of the proposed schemes. The discussion on analytical as well as numerical results sheds some light on designing practical network utility maximization protocols.</p>