Cloud computing (CC) is on-demand accessibility of network resources, especially datastorage and processing power, without special and direct management by the users. CC recentlyhas emerged as a set of public and private datacenters that offers the client a single platform acrossthe Internet. Edge computing is an evolving computing paradigm that brings computation andinformation storage nearer to the end-users to improve response times and spare transmissioncapacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones.However, CC and edge computing have security challenges, including vulnerability for clients andassociation acknowledgment, that delay the rapid adoption of computing models. Machine learning(ML) is the investigation of computer algorithms that improve naturally through experience. In thisreview paper, we present an analysis of CC security threats, issues, and solutions that utilizedone or several ML algorithms. We review different ML algorithms that are used to overcomethe cloud security issues including supervised, unsupervised, semi-supervised, and reinforcementlearning. Then, we compare the performance of each technique based on their features, advantages,and disadvantages. Moreover, we enlist future research directions to secure CC models.

In this paper, the role of adaptive group cohesion in a cooperative multi-agent source localization problem is investigated. A distributed source localization algorithm is presented for a homogeneous team of simple agents. An agent uses a single sensor to sense the gradient and two sensors to sense its neighbors. The algorithm is a set of individualistic and social behaviors where the individualistic behavior is as simple as an agent keeping its previous heading and is not self-sufficient in localizing the source. Source localization is achieved as an emergent property through agent’s adaptive interactions with the neighbors and the environment. Given a single agent is incapable of localizing the source, maintaining team connectivity at all times is crucial. Two simple temporal sampling behaviors, intensity-based-adaptation and connectivity-based-adaptation, ensure an efficient localization strategy with minimal agent breakaways. The agent behaviors are simultaneously optimized using a two phase evolutionary optimization process. The optimized behaviors are estimated with analytical models and the resulting collective behavior is validated against the agent’s sensor and actuator noise, strong multi-path interference due to environment variability, initialization distance sensitivity and loss of source signal.

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