Mario Di Francesco

Energy conservation in wireless sensor networks: A survey

In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.

Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey

Wireless sensor networks (WSNs) have emerged as an effective solution for a wide range of applications. Most of the traditional WSN architectures consist of static nodes which are densely deployed over a sensing area. Recently, several WSN architectures based on mobile elements (MEs) have been proposed. Most of them exploit mobility to address the problem of data collection in WSNs. In this article we first define WSNs with MEs and provide a comprehensive taxonomy of their architectures, based on the role of the MEs. Then we present an overview of the data collection process in such a scenario, and identify the corresponding issues and challenges. On the basis of these issues, we provide an extensive survey of the related literature. Finally, we compare the underlying approaches and solutions, with hints to open problems and future research directions.

A Comprehensive Analysis of the MAC Unreliability Problem in IEEE 802.15.4 Wireless Sensor Networks

Wireless Sensor Networks (WSNs) represent a very promising solution in the field of wireless technologies for industrial applications. However, for a credible deployment of WSNs in an industrial environment, four main properties need to be fulfilled, i.e., energy efficiency, scalability, reliability, and timeliness. In this paper, we focus on IEEE 802.15.4 WSNs and show that they can suffer from a serious unreliability problem. This problem arises whenever the power management mechanism is enabled for energy efficiency, and results in a very low packet delivery ratio, also when the number of sensor nodes in the network is very low (e.g., 5). We carried out an extensive analysis-based on both simulation and experiments on a real WSN-to investigate the fundamental reasons of this problem, and we found that it is caused by the contention-based Medium Access Control (MAC) protocol used for channel access and its default parameter values. We also found that, with a more appropriate MAC parameters setting, it is possible to mitigate the problem and achieve a delivery ratio up to 100%, at least in the scenarios considered in this paper. However, this improvement in communication reliability is achieved at the cost of an increased latency, which may not be acceptable for industrial applications with stringent timing requirements. In addition, in some cases this is possible only by choosing MAC parameter values formally not allowed by the standard.

Reliable and energy-efficient data collection in sparse sensor networks with mobile elements

Sparse wireless sensor networks (WSNs) are emerging as an effective solution for a wide range of applications, especially for environmental monitoring. In many scenarios, a moderate number of sparsely deployed nodes can be sufficient to get the required information about the sensed phenomenon. To this end, special mobile elements, i.e. mobile data collectors (MDCs), can be used to get data sampled by sensor nodes. In this paper we present an analytical evaluation of the data collection performance in sparse WSNs with MDCs. Our main contribution is the definition of a flexible model which can derive the total energy consumption for each message correctly transferred by sensors to the MDC. The obtained energy expenditure for data transfer also accounts for the overhead due to the MDC detection when sensor nodes operate with a low duty cycle. The results show that a low duty cycle is convenient and allows a significant amount of correctly received messages, especially when the MDC moves with a low speed. When the MDC moves fast, depending on its mobility pattern, a low duty cycle may not always be the most energy efficient option.

The MAC unreliability problem in IEEE 802.15.4 wireless sensor networks

In recent years, the number of sensor network deployments for real-life applications has rapidly increased and it is expected to expand even more in the near future. Actually, for a credible deployment in a real environment three properties need to be fulfilled, i.e., energy efficiency, scalability and reliability. In this paper we focus on IEEE 802.15.4 sensor networks and show that they can suffer from a serious MAC unreliability problem, also in an ideal environment where transmission errors never occur. This problem arises whenever power management is enabled - for improving the energy efficiency - and results in a very low delivery ratio, even when the number of nodes in the network is very low (e.g., 5). We carried out an extensive analysis, based on simulations and real measurements, to investigate the ultimate reasons of this problem. We found that it is caused by the default MAC parameter setting suggested by the 802.15.4 standard. We also found that, with a more appropriate parameter setting, it is possible to achieve the desired level of reliability (as well as a better energy efficiency). However, in some scenarios this is possible only by choosing parameter values formally not allowed by the standard.

An adaptive strategy for energy-efficient data collection in sparse wireless sensor networks

Sparse wireless sensor networks (WSNs) are being effectively used in several applications, which include transportation, urban safety, environment monitoring, and many others. Sensor nodes typically transfer acquired data to other nodes and base stations. Such data transfer operations are critical, especially in sparse WSNs with mobile elements. In this paper, we investigate data collection in sparse WSNs by means of special nodes called Mobile Data Collectors (MDCs), which visit sensor nodes opportunistically to gather data. As contact times and other information are not known a priori, the discovery of an incoming MDC by the static sensor node becomes a critical task. Ideally, the discovery strategy should be able to correctly detect contacts while keeping a low energy consumption. In this paper, we propose an adaptive discovery strategy that exploits distributed independent reinforcement learning to meet these two necessary requirements. We carry out an extensive simulation analysis to demonstrate the energy efficiency and effectiveness of the proposed strategy. The obtained results show that our solution provides superior performance in terms of both discovery efficiency and energy conservation.

Reliability and energy efficiency in multi-hop IEEE 802.15.4/ZigBee Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are a very appealing solution for many practical applications. Recently, WSNs have also been deployed in industrial scenarios, even for critical applications. Two major requirements are needed for an effective deployment of WSNs in such scenarios. The first is energy efficiency, as a network lifetime in the order of months or years is usually required. The other is reliability, since an even moderate message loss cannot be tolerated in critical applications. In this paper we evaluate the performance of the IEEE 802.15.4 standard in multi-hop WSNs where sleep/wakeup scheduling protocols are used for energy conservation. We show through extensive simulation results that the MAC parameter settings significantly impact on the performance. We demonstrate how an appropriate tuning of the MAC parameters can improve the reliability of communications, resulting in a very high delivery ratio. In addition, our solution also obtains a low energy expenditure.

A framework for Resource-Aware Data Accumulation in sparse wireless sensor networks

Wireless sensor networks (WSNs) have become an enabling technology for a wide range of applications. In contrast with traditional scenarios where static sensor nodes are densely deployed, a sparse WSN architecture can also be used in many cases. In a sparse WSN, special mobile data collectors (MDCs) are used to gather data from ordinary sensor nodes. In general, sensor nodes do not know when they will be in contact with the MDC, hence they need to discover its presence in their communication range. To this end, discovery mechanisms based on periodic listening and a duty-cycle have shown to be effective in reducing the energy consumption of sensor nodes. However, if not properly tuned, such mechanisms can hinder the data collection process. In this paper, we introduce a Resource-Aware Data Accumulation (RADA), a novel and lightweight framework which allows nodes to learn the MDC arrival pattern, and tune the discovery duty-cycle accordingly. Furthermore, RADA is able to adapt to changes in the operating conditions, and is capable of effectively supporting a number of different MDC mobility patterns. Simulation results show that our solution obtains a higher discovery efficiency and a lower energy consumption than comparable schemes.

An adaptive algorithm for dynamic tuning of MAC parameters in IEEE 802.15.4/ZigBee sensor networks

Recent studies have highlighted that IEEE 802.15.4 based wireless sensor networks (WSNs) suffer from a severe unreliability problem due to the default MAC parameters setting suggested by the standard, although with a more appropriate choice it is possible to achieve the desired reliability and better energy efficiency. However, such setting is strictly related to the operating conditions which, in general, vary over time and thus cannot be predicted in advance (i.e., before the deployment). In this paper, we propose an ADaptive Access Parameters Tuning (ADAPT) algorithm for dynamically adjusting the MAC parameters, based on the desired level of reliability and actual operating conditions experienced by the sensor nodes. Simulation experiments demonstrate that the ADAPT algorithm is able to provide the desired reliability with a very low energy expenditure, even under operating conditions that dynamically change with time.

A Storage Infrastructure for Heterogeneous and Multimedia Data in the Internet of Things

The Internet of Things (IoT) consists of networked objects deployed worldwide and connected over the Internet. As a consequence, the major aspects of the IoT are represented by the heterogeneity and the huge number of the participating devices. These aspects also constitute the major challenges in the definition of a storage infrastructure suitable for IoT applications. In this paper, we introduce a novel data model and storage infrastructure for the IoT to address these challenges. Different from other works in the literature, we exploit a document-oriented approach and show how it is suitable to support both heterogeneous and multimedia data. Our solution is built on top of the CouchDB database server, offers a Restful API, and provides a rich set of features targeted to IoT applications. Moreover, we devise optimized schemes for uploading documents which are specifically tailored to resource-constrained IoT devices. We evaluate our proposed schemes both analytically and with experiments in a real system.