In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.

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Phishing emails have permeated our digital communication, taking advantage of vulnerabilities that the information technology system poses to users. Given the potential for further cybersecurity incidents, theft of personally identifiable information, and damage to organizations’ assets, cybersecurity professionals have implemented various mitigation practices to combat phishing emails. This paper categorizes current mitigation practices in relation to a sequential schema adopted from the situational crime prevention approach, so as to enable a more organized and strategic assessment of human and environmental vulnerabilities. Our model could be useful for cybersecurity professionals to further advance mitigation measures as an incident progresses and for criminologists and other academic researchers to reduce the severity of subsequent criminal incidents.

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