Raquel A. F. Mini

Prediction-based energy map for wireless sensor networks

A fundamental issue in the design of a wireless sensor network is to devise mechanisms to make efficient use of its energy, and thus, extend its lifetime. The information about the amount of available energy in each part of the network is called the energy map and can be useful to increase the lifetime of the network. In this paper, we address the problem of constructing the energy map of a wireless sensor network using prediction-based approach. Simulation results compare the performance of a prediction-based approach with a naive one in which no prediction is used. Results show that the prediction-based approach outperforms the naive in a variety of parameters. We also investigate the possibility of sampling the energy information in some nodes in the network in order to diminish the number of energy information packets. Results show that the use of sampling techniques produce more constant error curves.

Dynamic Power Management in Wireless Sensor Networks: An Application-Driven Approach

Energy is a limited resource in wireless sensor networks. In fact, the reduction of power consumption is crucial to increase the lifetime of low power sensor networks. Several approaches on dynamic power management have contributed to reduce the power consumption, but few of them consider the application constraints to optimize it. In this paper, we propose a new application-driven power management approach, where we model the sensor node operation and the application constraints using the hybrid automata framework. We also model a real sensor network application for fire detection and we show the performance of our approach in terms of energy drop, comparing it to an Ideal Model and a Naive approach.

The distinctive design characteristic of a wireless sensor network: the energy map

The key challenge in the design of a wireless sensor network is maximizing its lifetime. This is a fundamental problem and new protocol engineering principles need to be established in order to achieve this goal. The information about the amount of available energy in each part of the network is called the energy map and can be useful to increase the lifetime of the network. In this paper, we propose using the energy map as a protocol engineering principle for this kind of network. We argue that an energy map can be the basis for the entire design trajectory including all functionalities to be included in a wireless sensor network. Furthermore, we show how to construct an energy map using both probabilistic and statistical prediction-based approaches. Simulation results compare the performance of these approaches with a naive one in which no prediction is used. The experiments performed use an energy dissipation model that we have proposed to simulate the behavior of a sensor node in terms of energy consumption. The results show that prediction-based approaches outperform the naive in a variety of parameters.

On the design of resilient heterogeneous wireless sensor networks based on small world concepts

In this work, we propose on-line models to design heterogeneous sensor network topologies with small world features. The proposed model takes into account the data communication flow in this kind of network to create network shortcuts toward the sink node in such a way that the communication between the sink and the sensor nodes is optimized. The endpoints of these shortcuts are nodes with more powerful hardware, leading to a heterogeneous sensor network. We evaluate the on-line models and show that they present the same small world features observed in the theoretical models. When the shortcuts are created toward the sink node, with a small number of powerful sensors, the network presents better small world features and interesting tradeoffs between energy and latency in the data communication when compared with the Random Additional Model. We evaluate the resilience of the on-line models considering general and specific failures and, in both cases, the proposed model is more robust and presents a graceful degradation of the network latency, which shows the resilience of those models.

Data communication in VANETs: Protocols, applications and challenges

Abstract VANETs have emerged as an exciting research and application area. Increasingly vehicles are being equipped with embedded sensors, processing and wireless communication capabilities. This has opened a myriad of possibilities for powerful and potential life-changing applications on safety, efficiency, comfort, public collaboration and participation, while they are on the road. Although, considered as a special case of a Mobile Ad Hoc Network, the high but constrained mobility of vehicles bring new challenges to data communication and application design in VANETs. This is due to their highly dynamic and intermittent connected topology and different application’s QoS requirements. In this work, we survey VANETs focusing on their communication and application challenges. In particular, we discuss the protocol stack of this type of network, and provide a qualitative comparison between most common protocols in the literature. We then present a detailed discussion of different categories of VANET applications. Finally, we discuss open research problems to encourage the design of new VANET solutions.

Data dissemination in autonomic wireless sensor networks

In this paper, a new data dissemination algorithm for wireless sensor networks is presented. The key idea of the proposed solution is to combine concepts presented in trajectory-based forwarding with the information provided by the energy map of the network to determine routes in a dynamic fashion, according to the energy level of the sensor nodes. This is an important feature of an autonomic system, which must have the capacity of adapting its behavior according to its available resources. Simulation results revealed that the energy spent with the data dissemination activity can be concentrated on nodes with high-energy reserves, whereas low-energy nodes can use their energy only to perform sensing activity or to receive information addressed to them. In this manner, partitions of the network due to nodes that ran out of energy can be significantly delayed and the network lifetime extended.

A distributed data storage protocol for heterogeneous wireless sensor networks with mobile sinks

Abstract This paper presents ProFlex, a distributed data storage protocol for large-scale Heterogeneous Wireless Sensor Networks (HWSNs) with mobile sinks. ProFlex guarantees robustness in data collection by intelligently managing data replication among selected storage nodes in the network. Contrarily to related protocols in the literature, ProFlex considers the resource constraints of sensor nodes and constructs multiple data replication structures, which are managed by more powerful nodes. Additionally, ProFlex takes advantage of the higher communication range of such powerful nodes and uses the long-range links to improve data distribution by storage nodes. When compared with related protocols, we show through simulation that Proflex has an acceptable performance under message loss scenarios, decreases the overhead of transmitted messages, and decreases the occurrence of the energy hole problem. Moreover, we propose an improvement that allows the protocol to leverage the inherent data correlation and redundancy of wireless sensor networks in order to decrease even further the protocol’s overhead without affecting the quality of the data distribution by storage nodes.

Data dissemination based on the energy map

One of the most important resources in wireless sensor networks is energy, since, in general, batteries cannot be recharged. The information about the amount of energy available at each part of the network is called the energy map and can be explored by data dissemination algorithms. In this work, a new data dissemination algorithm for wireless sensor networks is proposed. The key idea is to combine concepts presented in trajectory-based forwarding with the information provided by the energy map to determine routes in a dynamic fashion. Simulation results reveal that the energy spent with data dissemination activity can be concentrated on nodes with high energy reserves, whereas low-energy nodes can use their energy only to perform sensing activity. In this manner, partitions of the network due to nodes that run out of energy can be significantly delayed and the network lifetime extended.

Prediction-Based Energy Map for Wireless Sensor Networks

The key challenge in the design of wireless sensor networks is maximizing their lifetime. The information about the amount of available energy in each part of the network is called the energy map and can be useful to increase the lifetime of the network. In this paper, we address the problem of constructing the energy map of a wireless sensor network using prediction-based approaches. We also present an energy dissipation model that is used to simulate the behavior of a sensor node in terms of energy consumption. Simulation results compare the performance of the prediction-based approaches with a naive one in which no prediction is used. The results show that the prediction-based approaches outperform the naive in a variety of parameters.

Energy in Wireless Sensor Networks

Wireless sensor networks (WSNs) are composed of cooperating sensor nodes that can perceive the environment to monitor physical phenomena and events of interest. WSNs are envisioned to be applied in different applications, including, among others, habitat, environmental and industrial monitoring, which have great potential benefits for the society as a whole. Each sensor node typically includes a sensing component responsible for data acquisition from the physical environment. The node also has a processing unit for local data processing and storage, a wireless communication interface for data communication between nodes, and a power source to supply the energy used by the node to perform the programmed task. The power source often consists of a battery with a limited energy capacity. In many applications, sensor nodes may not be easily accessible because of the locations where they are deployed or the large scale of such networks. In both cases, network maintenance for energy replenishment becomes impractical. Furthermore, in case a sensor battery needs to be frequently replaced the main advantages of a wireless sensor network are lost, i.e., its operational cost, freedom from wiring constraints, and possibly more important, many sensing applications may become impractical.