Virginia Pilloni

The Virtual Object as a Major Element of the Internet of Things: A Survey

The Internet of Things (IoT) paradigm has been evolving toward the creation of a cyber-physical world where everything can be found, activated, probed, interconnected, and updated, so that any possible interaction, both virtual and/or physical, can take place. A Crucial concept of this paradigm is that of the virtual object, which is the digital counterpart of any real (human or lifeless, static or mobile, solid or intangible) entity in the IoT. It has now become a major component of the current IoT platforms, supporting the discovery and mash up of services, fostering the creation of complex applications, improving the objects energy management efficiency, as well as addressing heterogeneity and scalability issues. This paper aims at providing the reader with a survey of the virtual object in the IoT world. Virtualness is addressed from several perspectives: historical evolution of its definitions, current functionalities assigned to the virtual object and how they tackle the main IoT challenges, and major IoT platforms, which implement these functionalities. Finally, we illustrate the lessons learned after having acquired a comprehensive view of the topic.

Deployment of Distributed Applications in Wireless Sensor Networks

The increase in computation and sensing capabilities as well as in battery duration of commercially available Wireless Sensors Network (WSN) nodes are making the paradigm of an horizontal ambient intelligence infrastructure feasible. Accordingly, the sensing, computing and communicating infrastructure is set with a programmable middleware that allows for quickly deploying different applications running on top of it so as to follow the changing ambient needs. In this scenario, we face the problem of setting up the desired application in complex scenarios with hundreds of nodes, which consists of identifying which actions should be performed by each of the nodes so as to satisfy the ambient needs while minimizing the application impact on the infrastructure battery lifetime. Accordingly, we approach the problem by considering every possible decomposition of the application’s sensing and computing operations into tasks to be assigned to each infrastructure component. The contribution of energy consumption due to the performance of each task is then considered to compute a cost function, allowing us to evaluate the viability of each deployment solution. Simulation results show that our framework results in considerable energy conservation with respect to sink-oriented or cluster-oriented deployment approaches, particularly for networks with high node densities, non-uniform energy consumption and initial energy, and complex actions.

The problem of task allocation in the Internet of Things and the consensus-based approach

The realization of the Internet of Things (IoT) paradigm relies on the implementation of systems of cooperative intelligent objects with key interoperability capabilities. One of these interoperability features concerns the cooperation among nodes towards a collaborative deployment of applications taking into account the available resources, such as electrical energy, memory, processing, and object capability to perform a given task, which are often limited.In this paper, firstly, we define the issue related to resource allocation for the deployment of distributed applications in the IoT, and we describe the architecture and functionalities of a relevant middleware that represents a possible solution to this issue. Secondly, we propose a consensus protocol for the cooperation among network objects in performing the target application, which aims to distribute the burden of the application execution, so that resources are adequately shared. We demonstrate that, using the proposed protocol, the network converges to a solution where resources are homogeneously allocated among nodes. Performance evaluation of experiments in simulation mode and in real scenarios show that the algorithm converges with a percentage error of about 5% with respect to the optimal allocation obtainable with a centralized approach.

TAN: A Distributed Algorithm for Dynamic Task Assignment in WSNs

We consider the scenario of wireless sensor networks where a given application has to be deployed and each application task has to be assigned to each node in the best possible way. Approaches where decisions on task execution are taken by a single central node can avoid the exchange of data packets between task execution nodes but cannot adapt to dynamic network conditions, and suffer from computational complexity. To address this issue, in this paper, we propose an adaptive and decentralized task allocation negotiation algorithm (TAN) for cluster network topologies. It is based on noncooperative game theory, where neighboring nodes engage in negotiations to maximize their own utility functions to agree on which of them should execute single application tasks. Performance is evaluated in a city scenario, where the urban streets are equipped with different sensors and the application target is the detection of the fastest way to reach a destination, and in random WSN scenarios. Comparisons are made with three other algorithms: 1) baseline setting with no task assignment to multiple nodes; 2) centralized task assignment lifetime optimization; and 3) a dynamic distributed algorithm, DLMA. The result is that TAN outperforms these algorithms in terms of application completion time and average energy consumption.

Cooperative task assignment for distributed deployment of applications in WSNs

Nodes in Wireless Sensor Networks (WSNs) are becoming more and more complex systems with the capabilities to run distributed structured applications. Which single task should be implemented by each WSN node needs to be decided by the application deployment strategy by taking into account both network lifetime and execution time requirements. In this paper, we propose an adaptive decentralised algorithm based on noncooperative game theory, where neighbouring nodes negotiate among each other to maximize their utility function. We then prove that an increment of the nodes utility corresponds to the same increment of the utility for the whole network. Simulation results show significant performance improvement with respect to existing algorithms.

A decentralized lifetime maximization algorithm for distributed applications in Wireless Sensor Networks

We consider the scenario of a Wireless Sensor Networks (WSN) where the nodes are equipped with a programmable middleware that allows for quickly deploying different applications running on top of it so as to follow the changing ambient needs. We then address the problem of finding the optimal deployment of the target applications in terms of network lifetime. We approach the problem considering every possible decomposition of an applications sensing and computing operations into tasks to be assigned to each infrastructure component. The contribution of energy consumption due to the energy cost of each task is then considered into local cost functions in each node, allowing us to evaluate the viability of the deployment solution. The proposed algorithm is based on an iterative and asynchronous local optimization of the task allocations between neighboring nodes that increases the network lifetime. Simulation results show that our framework leads to considerable energy saving with respect to both sink-oriented and cluster-oriented deployment approaches, particularly for networks with high node densities and non-uniform energy consumption or initial battery charge.

Dynamic Involvement of Real World Objects in the IoT: A Consensus-Based Cooperation Approach

A significant role in the Internet of Things (IoT) will be taken by mobile and low-cost unstable devices, which autonomously self-organize and introduce highly dynamic and heterogeneous scenarios for the deployment of distributed applications. This entails the devices to cooperate to dynamically find the suitable combination of their involvement so as to improve the system reliability while following the changes in their status. Focusing on the above scenario, we propose a distributed algorithm for resources allocation that is run by devices that can perform the same task required by the applications, allowing for a flexible and dynamic binding of the requested services with the physical IoT devices. It is based on a consensus approach, which maximizes the lifetime of groups of nodes involved and ensures the fulfillment of the requested Quality of Information (QoI) requirements. Experiments have been conducted with real devices, showing an improvement of device lifetime of more than 20%, with respect to a uniform distribution of tasks.

IoT Architecture for a Sustainable Tourism Application in a Smart City Environment

In the past few years, the Smart Cities concept has become one of the main driving forces for the urban transition towards a low carbon environment, sustainable economy, and mobility. Tourism, as one of the fastest growing industries, is also an important generator of carbon emissions; therefore, the recently emerging sustainable tourism concept is envisioned as an important part of the Smart Cities paradigm. Within this context, the Internet-of-Things (IoT) concept is the key technological point for the development of smart urban environments through the use of aggregated data, integrated in a single decisional platform. This paper performs the first analysis on the feasibility of the use of an IoT approach and proposes a specific architecture for a sustainable tourism application. The architecture is tailored for the optimisation of the movement of cruise ship tourists in the city of Cagliari (Italy), by taking into consideration factors such as transport information and queue waiting times. A first set of simulations is performed using 67-point of interest, real transportation data, and an optimisation algorithm.

Deployment of Applications in Wireless Sensor Networks: A Gossip-Based Lifetime Maximization Approach

In this brief, a wireless sensor network in which the nodes are equipped with a programmable middleware that allows the quick deployment of different applications is considered. We propose a distributed method to extend the network lifetime by managing how tasks are assigned to different nodes. A detailed model of energy consumption of the wireless sensor nodes is provided and a gossip-based algorithm that only exploits iterative and asynchronous optimizations involving local information between neighboring nodes to maximize the global network lifetime is proposed. Experimental results and simulations performed on the embedded hardware show that our framework can provide a significant lifetime extension, particularly for networks with high node densities and nonuniform energy consumption or initial battery charge.

A novel Smart Home Energy Management system: Cooperative neighbourhood and adaptive renewable energy usage

Energy usage optimization in Smart Homes is a critical problem: over 30% of the energy consumption of the world resides in the residential sector. Usage awareness and manual appliance control alone are able to reduce consumption by 15%. This result could be improved if appliance control is automatic, especially if renewable sources are present locally. In this paper, a Smart Home Energy Management system that aims at automatically controlling appliances in groups of smart homes belonging to the same neighborhood is proposed. Not only is electric power distribution considered, but also renewable energy sources such as wind micro-turbines and solar panels. The proposed strategy relies on two algorithms. The Cost Saving Task Scheduling algorithm is aimed at scheduling high-power controllable loads during off-peak hours, taking into account the expected usage of the non-controllable appliances such as fridge, oven, etc. This algorithm is run whenever a new need of energy from a controllable load is detected. The Renewable Source Power Allocation algorithm re-allocated the starting time of controllable loads whenever surplus of renewable source power is detected making use of a distributed max-consensus negotiation. Performance evaluation of the algorithms tested proves that the proposed approach provides an energy cost saving that goes between 35% and 65% with reference to the case where no automatic control is used.