In this paper, we propose BeeCast, a collaborative video streaming system that facilitates collaborative video streaming for a group of mobile users with limited Internet connectivity. The novelty of the proposed system is the ability to watch the video on a shared screen or to watch the video on multiple screens. The latter option entails proposing a method to exchange the downloaded video segments among the users using device-to-device communication. The proposed system is composed of two components: BeeBuzzer, and BeePlanner. The BeeBuzzer component manages and coordinates the segment exchange among devices, while BeePlanner component enhances the overall Quality of Experience (QoE) through effective segment assignments decisions for each user. Simulation results show that using BeeCast in an unstable network produces a more consistent QoE than individual streaming while eliminating 80% of redundant network traffic.

The performance of distributed streaming systems relies on how even the workload is distributed among their machines. However, data and query workloads are skewed and change rapidly. Therefore, multiple adaptive load-balancing mechanisms have been proposed in the literature to rebalance distributed streaming systems according to the changes in their workloads. This paper introduces a novel attack model that targets adaptive load-balancing mechanisms of distributed streaming systems. The attack reduces the throughput and the availability of the system by making it stay in a continuous state of rebalancing. This paper proposesGuard, a component that detects and blocks attacks that target the adaptive load balancing of distributed streaming systems. Guard uses an unsupervised machine-learning technique to detect malicious users that are involved in the attack. Guard does not block any user unless it detects that the user is malicious. Guard does not depend on a specific application. Experimental evaluation for a high-intensity attack illustrates that Guard improves the throughput and the availability of the system by 85% and 86%, respectively. Moreover, Guard improves the minimum availability that the attacker achieves by 325%.

Recently, cyber security in power systems has captured significant interest. This is because the world has seen a surge in cyber attacks on power systems. One of the prolific cyber attacks in modern power systems are False Data Injection Attacks (FDIA). In this paper, we analyzed the impact of FDIA on the operation cost of power systems. Also, we introduced a novel Advanced Persistent Threat (APT) based attack strategy that maximizes the operating costs when attacking specific nodes in the system. We model the attack strategy using an optimization problem and use metaheuristics algorithms to solve the optimization problem and execute the attack. We have found that our attacks can increase the power generation cost by up to 15.6%, 60.12%, and 74.02% on the IEEE 6-Bus systems, 30-Bus systems, and 118-Bus systems, respectively, as compared to normal operation.

In this paper, we propose BeeCast, a collaborative video streaming system, that allows a group of mobile users to cooperate in streaming video content. The novelty of the proposed system is that it allows each user to watch the same video content on their own device at the same time. This entails proposing a method to exchange the downloaded video chunks among the users using device-to-device communication. The proposed system is composed of two components: BeeBuzzer, and BeePlanner. The BeePlanner component performs chunk scheduling, while the BeeBuzzer component manages and coordinates the chunk exchange among devices.

In this paper, we propose a cooperative multi-user video streaming system, termed GiantClient, for videos encoded using scalable video coding (SVC). The proposed system allows a group of users to watch a video on a single screen. The users, who may have different data plans from different carriers or different levels of energy, can collaborate to fetch the SVC-encoded video at high quality and avoid running into re-buffering. Using SVC, each layer of every chunk of the video can be fetched by only one of the cooperating users. Therefore, we formulate the streaming problem that obtains the quality and the fetching policy decisions as an optimization problem. The objective is to optimize a novel quality-of-experience metric that maintains a tradeoff between maximizing the quality of every chunk and ensuring fairness among all video chunks for the minimum re-buffering time. The problem is constrained with the available bandwidth, the chunk deadlines, and the imposed maximum contribution constraints by users. Moreover, we propose a low-complexity algorithm to solve the proposed optimization problem. A real implementation of the system with real SVC-encoded videos and real bandwidth traces reveal the robustness and performance of the proposed algorithm.

There has been significant interest in the development of anonymization schemes for publishing graph data. However, privacy is a major concern in dealing with graph data. In this paper, an integrated framework for ensuring privacy in the presence of an authorization mechanism is proposed. Access control mechanisms provide additional safeguard against data breaches and ensure that only authorized information is available to end-users based on their assigned roles. The integrated framework highlights a tradeoff between privacy and authorized privileges. To attain a pre-specified privacy level, access privileges might need to be relaxed. For the proposed framework, we formulate the k-anonymous Bi-objective Graph Partitioning (k-BGP) problem and provide its hardness results. Heuristics solutions are developed to solve the constraint problem. The framework provides an anonymous view based on the target class of role-based workloads for graph data. The proposed heuristics are empirically evaluated and a detailed security analysis of the framework in terms of risk associated with re-identification attack is conducted.

In this article, we propose a threat management system (TMS) for Data-centric Internet-of-Things-based Collaborative Systems (DIoTCSs). In particular, we focus on tampering attacks that target shared databases and can affect the execution of the DIoTCS services. The novelty of the proposed system is to isolate the damage caused by tampering attacks into data partitions. We formulate the partitioning problem as a cost-driven optimization problem, prove its NP-hardness, and propose two polynomial-time heuristics. We evaluate a TMS experimentally and demonstrate that intelligent partitioning of the database improves the overall availability of the DIoTCS.

Wireless Sensor Networks (WSN) are expected to play a vital role in the exploration and monitoring of underwater areas which are not easily reachable by humans. However, underwater communication via acoustic waves is subject to several performance limitations that are very different from those used for terresstrial networks. In this paper, we investigate node placement for building an initial underwater WSN infrastructure. We formulate this problem as a nonlinear mathematical program with the objective of minimizing the total transmission loss under a given number of sensor nodes and targeted coverage volume. The obtained solution is the location of each node represented via a truncated octahedron to fill out the 3D space. Experiments are conducted to verify the proposed formulation, which is solved using Matlab optimization tool. Simulation is also conducted using an ns-3 simulator, and the simulation results are consistent with the obtained results from mathematical model with less than 10% error.