In this work, we address the problem of offloading tasks in a mobile cloud environment by proposing a three-tier architecture. The proposed architecture, named as SOBDO, tries to offload the tasks to nearby mobile devices or cloudlets before offloading to a distant cloud. Nearby mobile devices and cloudlets are considered as the components of first two tiers, whereas a distant cloud builds up the third tier. We apply the concepts from Auction theory to optimally assign a task to one of the devices, cloudlets, or cloud, based on different requirements, such as latency and energy consumption. The performance of the proposed architecture is evaluated by simulation and testbed experimentation, and the results show that our approach yields satisfactory outcomes.

In this work, we study the adaptation of Named Data Networking (NDN)-based named content searching in Opportunistic Mobile Networks (OMNs) with a goal to replicate content requests to a suitable set of nodes that can help improve users’ experience in terms of requests satisfaction and latency. We propose a scheme, content searching as regret minimization (CHARM), based on the technique of random regret minimization (RRM). In CHARM, content interest replication and non-replication choices are expressed in terms of several measurable attributes. Regrets associated with the two choices indicate whether or not an interest should be replicated. Moreover, to reduce overhead, CHARM uses dynamic time-to-live (TTL) adaptation, where the lifetime of a message is proactively scaled down.

In this work, we combine the natural mobility of the human in OMNs, together with intentional explorations and consider the case where the human users in an OMN undergo explorations. The objective of this work is two-fold – 1) Establishing that limited explorations of the users can help in enhancing the performance of OMNs, and 2) Formulating a method to decide whether or not a user should undergo exploration. In this regard, we propose two schemes based on Prospect Theory (PT) and Expected Utility Theory (EUT).

In this work, we address the problem of Named Data Networking (NDN)-based fake content dissemination in Opportunistic Mobile Networks (OMNs), where nodes have intermittent connectivity and typically lack in end-to-end communication paths. We consider four different behaviors of the Fake Content Providers (FCPs) – referred to as threat scenarios – depending on whether or not they always respond to all requests with fake contents. We analyze these distinct threat scenarios, and characterize the relative performance degradation arising because of them. To mitigate the adverse effects of FCPs, we propose two schemes, wherein the identified FCPs are blacklisted permanently or temporarily, and communication with them is restricted.

In this paper, we propose a scheme, OPTIVE, for obtaining the optimal configuration of a virtual sensor in the mobile sensor-cloud (MSC) architecture. The proposed scheme is capable of selecting the physical sensor nodes to form a virtual sensor (VS), based on the sensing area coverage in the application region. We use Markov Decision Process (MDP) to select the optimal mobile sensor nodes among the available ones, for configuring the VS in the application area. OPTIVE selects the optimal physical sensor node from multiple sensor nodes that may be present in the application region to allocate in the VS for ensuring uninterrupted services to the end-users.

In this paper, we propose that a plug-in hybrid electric vehicle (PHEV) may get energy from any of the available micro-grids within a coalition instantaneously without paying higher price. In this work, the problem of energy trading network topology control for PHEVs is studied as a multi-leader multi-follower Stackelberg game. In this game, each PHEV acts as a leader, and decides the amount of energy to be requested to the selected micro-grid. On the other hand, the micro-grids act as followers, need to decide the price per unit energy.

In this paper, the problem of energy distribution using virtual energy-cloud to the plug-in hybrid electric vehicles (PHEVs) is studied as a single leader multiple follower noncooperative Stackelberg game. In this game, the energy-cloud service provider acts as leader, and decides the price to be paid by each PHEV as per its usage. On the other hand, the PHEVs act as the followers, and need to decide the amount of energy to be consumed based on their requirements. Using variational inequality, it is shown that the proposed scheme, virtual energy cloud topology control (VELD) scheme has a generalized Nash equilibrium, which is also socially optimal. The proposed scheme, VELD, which enables the energy-cloud service provider and the PHEVs to reach the equilibrium state, is evaluated theoretically as well as through simulations.