Cloud computing is an essential technology for the future of the Information Technology (IT) industry. However, the cloud security level is identified as the biggest challenge facing cloud providers and a major concern for cloud adopters. Economic Denial of Sustainability (EDoS) attack is one of the major threats targeting the cloud. The EDoS attack exploits the cloud elasticity and auto scaling features to charge a cloud adopter bill an excessive amount of cost leading to large-scale service withdrawal or bankruptcy. A novel reactive approach referred to as the EDoS Attack Defense Shell (EDoS-ADS) is proposed to mitigate EDoS attacks while taking into account most of the existing mitigation techniques drawbacks. Specifically, the EDoS-ADS has the ability to identify the legitimacy of clients even if they belong to a Network Address Translation (NAT) based network. Thus, EDoS-ADS is the first known technique that effectively prevents an EDoS attack from blocking an entire NAT-based network from accessing the cloud. The EDoS-ADS effectiveness in terms of response time, CPU utilization, throughput, and cost is evaluated using a CloudSim simulator. The simulation results show that EDoS-ADS outperforms other mitigation techniques, and successfully differentiates between legitimate and attacker clients even when they belong to the same NAT-based network.

The rapid growth of technology has introduced robots into daily life, necessitating navigation frameworks that enable safe, human-friendly movement while accounting for social aspects. Such methods must also scale to situations with multiple humans and robots moving simultaneously. Recent advances in Deep Reinforcement Learning (DRL) have enabled policies that incorporate these norms into navigation. This work presents a socially aware navigation framework for mobile robots operating in environments shared with humans and other robots. The approach, based on single-agent DRL, models all interaction types between the ego robot, humans, and other robots. Training uses a reward function balancing task completion, collision avoidance, and maintaining comfortable distances from humans. An attention mechanism enables the framework to extract knowledge about the relative importance of surrounding agents, guiding safer and more efficient navigation. Our approach is tested in both dynamic and static obstacle environments. To improve training efficiency and promote socially appropriate behaviors, Imitation Learning is employed. Comparative evaluations with state-of-the-art methods highlight the advantages of our approach, especially in enhancing safety by reducing collisions and preserving comfort distances. Results confirm the effectiveness of our learned policy and its ability to extract socially relevant knowledge in human–robot environments where social compliance is essential for deployment.

Biological sensors (biosensors, for short) are tiny wireless devices attached or implanted into the body of a human or animal to monitor and detect abnormalities and then relay data to physician or provide therapy on the spot. They are distinguished from conventional sensors by their biologically derived sensing elements and by being temperature constrained. Biosensors generate heat when they transmit their measurements and when they are recharged by electromagnetic energy. The generated heat translates to a temperature increase in the tissues surrounding the biosensors. If the temperature increase exceeds a certain threshold, the tissues might be damaged. In this paper, we discuss the problem of finding an optimal policy for operating a rechargeable biosensor inside a temperature-sensitive environment characterized by a strict maximum temperature increase constraint. This problem can be formulated as a Markov Decision Process (MDP) and solved to obtain the optimal policy which maximizes the average number of samples that can be generated by the biosensor while observing the constraint on the maximum safe temperature level. In order to handle large-size MDP models, it is shown how operating policies can be obtained using Q-learning and heuristics. Numerical and simulation results demonstrating the performance of the different policies are presented.

Biological sensors are a very promising technology that will take healthcare to the next level. However, there are obstacles that must be overcome before the full potential of this technology can be realized. One such obstacle is that the heat generated by biological sensors implanted into a human body might damage the tissues around them. Dynamic sensor scheduling is one way to manage and evenly distribute the generated heat. In this paper, the dynamic sensor scheduling problem is formulated as a Markov decision process (MDP). Unlike previous works, the temperature increase in the tissues caused by the generated heat is incorporated into the model. The solution of the model gives an optimal policy that when executed will result in the maximum possible network lifetime under a constraint on the maximum temperature level tolerable by the patient's body. In order to obtain the optimal policy in a lesser amount of time, two specific types of states are aggregated to produce a considerably smaller MDP model equivalent to the original one. Numerical and simulation results are presented to show the validity of the model and superiority of the optimal policy produced by it when compared with two policies one of which is specifically designed for biological wireless sensor networks.

The rapid growth of technology has introduced robots into daily life, necessitating navigation frameworks that enable safe, human-friendly movement while accounting for social aspects. Such methods must also scale to situations with multiple humans and robots moving simultaneously. Recent advances in Deep Reinforcement Learning (DRL) have enabled policies that incorporate these norms into navigation. This work presents a socially aware navigation framework for mobile robots operating in environments shared with humans and other robots. The approach, based on single-agent DRL, models all interaction types between the ego robot, humans, and other robots. Training uses a reward function balancing task completion, collision avoidance, and maintaining comfortable distances from humans. An attention mechanism enables the framework to extract knowledge about the relative importance of surrounding agents, guiding safer and more efficient navigation. Our approach is tested in both dynamic and static obstacle environments. To improve training efficiency and promote socially appropriate behaviors, Imitation Learning is employed. Comparative evaluations with state-of-the-art methods highlight the advantages of our approach, especially in enhancing safety by reducing collisions and preserving comfort distances. Results confirm the effectiveness of our learned policy and its ability to extract socially relevant knowledge in human-robot environments where social compliance is essential for deployment.