with the increasing popularity of Internet of Things (IoT) applications in many different areas, the round-trip delay of data processing in the cloud affect the user's quality perception. But fortunately, fog computing aims to service users at the network edge similar to cloud services, which helps in supporting IoT in processing the data near to the end-user, especially for time-sensitive applications. It makes the resource allocation of application placement requests in a fog environment more necessary to satisfy the Quality of Experience (QoE) Influence Factors (IFs). In this paper, an IoT application placement algorithm based on the Multi-Dimensional QoE (MD-QoE) model is proposed in a fog computing environment. The algorithm is composed of two main phases. The first phase is to prioritize different IoT application placement requests depending on three main domains of IFs which are: Environment runtime context, application usage, and user expectations considering the Quality of Service (QoS) violation as a feedback. The second phase is to map and place the request to the appropriate fog node instance, depending on its proximity, computing capabilities, and expected response time. The proposed algorithm is evaluated by simulating a fog environment using iFogSim. Experimental results indicate that the proposed algorithm significantly improves the QoE in respect of application placement time, application delay, network usage, and power consumption. Therefore, the proposed algorithm can improve the overall system performance with a slight increasing in power consumption in fog control nodes.

Cloud computing is increasing rapidly as a successful paradigm presenting on-demand infrastructure, platform, and software services to clients. Load balancing is one of the important issues in cloud computing to distribute the dynamic workload equally among all the nodes to avoid the status that some nodes are overloaded while others are underloaded. Many algorithms have been suggested to perform this task. Recently, worldview is turning into a new paradigm for optimization search by applying the osmosis theory from chemistry science to form osmotic computing. Osmotic computing is aimed to achieve balance in highly distributed environments. The main goal of this paper is to propose a hybrid metaheuristics technique which combines the osmotic behavior with bio-inspired load balancing algorithms. The osmotic behavior enables the automatic deployment of virtual machines (VMs) that are migrated through cloud infrastructures. Since the hybrid artificial bee colony and ant colony optimization proved its efficiency in the dynamic environment in cloud computing, the paper then exploits the advantages of these bio-inspired algorithms to form an osmotic hybrid artificial bee and ant colony (OH_BAC) optimization load balancing algorithm. It overcomes the drawbacks of the existing bio-inspired algorithms in achieving load balancing between physical machines. The simulation results show that OH_BAC decreases energy consumption, the number of VMs migrations and the number of shutdown hosts compared to existing algorithms. In addition, it enhances the quality of services (QoSs) which is measured by service level agreement violation (SLAV) and performance degradation due to migrations (PDMs). INDEX TERMS Ant colony optimization, artificial bee colony, bio-inspired systems, cloud computing, load balancing, metaheuristics, osmotic computing.

Classical clustering protocols in wireless sensor networks (WSNs) assume that all nodes are equipped with the same amount of energy. As a result, they cannot take full advantage of the presence of node heterogeneity. In this study, a stable and energy-efficient clustering (SEEC) protocol for heterogeneous WSNs is proposed. In addition, the extension to multi-level of SEEC is presented. It depends on the network structure that is divided into clusters. Each cluster has a powerful advanced node and some normal nodes deployed randomly in this cluster. In the multi-level architectures, more powerful supper nodes are assigned to cover distant sensing areas. Each type of nodes has its role in the sensing, aggregation or transmission to the base station. At each level of heterogeneity, the optimum number of powerful nodes that achieves the minimum energy consumption of the network is obtained. The proposed protocol is a heterogeneous awareness to prolong the stability period, which is crucial for many applications. The performance of the proposed protocol is compared by existing homogeneous and heterogeneous protocols. Simulation results show that the proposed protocol provides a longer stability period, more energy efficiency and higher average throughput than the existing protocols.

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A Vehicular Ad-hoc Network (VANET) is a type of Mobile Ad-hoc Network (MANET) that is used to provide communications between nearby vehicles, and between vehicles and fixed infrastructure on the roadside. VANET is not only used for road safety and driving comfort but also for infotainment. Communication messages in VANET can be used to locate and track vehicles. Tracking can be beneficial for vehicle navigation using Location Based Services (LBS). However, it can lead to threats on location privacy of vehicle users; since it can profile them and track their physical location. Therefore, to successfully deploy LBS, user's privacy is one of major challenges that must be addressed. In this paper, we propose Privacy-Preserving Fully Homomorphic Encryption over Advanced Encryption Standard (P 2 FHE-AES) scheme for LBS query. This scheme is required for location privacy protection to encourage drivers to use this service without any risk of being pursued. It is implemented using Network Simulator (NS-2), Simulation of Urban Mobility (SUMO), and Cloud simulation (CloudSim). Analysis and evaluation results demonstrate that P 2 FHE-AES scheme can preserve the privacy of the drivers' future routes in an efficient and secure way. The results prove the feasibility and efficiency of P 2 FHE-AES scheme in terms of query's response time, query accuracy, throughput and query overhead.