The evolution of traditional electricity distribution infrastructures towards Smart Grid networks has generated the need to carry out new research. There are many fields that have attracted the attention of researchers, among which is the improvement of the performance of the so-called Neighborhood Area Networks (NAN). In this paper, three mechanisms for these kind of networks are proposed, implemented and evaluated: traffic differentiation, multichannel allocation and congestion control. These proposals have been evaluated in the context of a wireless mesh networks (WMN) made up by a set of smart meter devices, where various smart grids (SG) applications are sending their data traffics. Each SG application must meet its unique quality of service (QoS) requirements, such as reliability and delay. To evaluate the proposals, we have built some NAN scenarios by using the ns-3 simulator and its 802.11s basic model, which we have modified to implement our techniques. Compared with the basic Hybrid Wireless Mesh Protocol (HWMP), our modifications, multiple channel (M-HWMP) and congestion control (MC-HWMP), show improvements in terms of packet delivery ratio and transit time.
This paper investigates and proposes real-time power-splitting approach with efficient relay selection scheme for multi-terminal decode and forward ultrawideband(DF-UWB)relay network.The channel state information(CSI)technique allows for the adaptive power splitting (ASP) approach to be investigated by analyzing the adaptation of the multi-terminal relay nodes to power-splitting (PS)ratio with verifiable accomplishment in the gap between the energy-saved and the re-transmitted signal. Since the relays are multi-terminal and act as intermediaries between the source and the destination nodes,an optimal relay selection scheme is introduced to select the best relay that forwards the decoded information packets from the source to the destination nodes. Furthermore, the need to maintain better outage performance w.r.t the multi-terminal relay network scheme is demonstrated in the simulation works while an optimized Dinkelbach(DB)iterative algorithm is introduced to maintain minimal end-to-end signal-to-noise ratio (SNR)for the multi-terminal relay links.
Nowadays, cities are facing an increasing number of bikes used by citizens therefore the need of monitoring and managing their traffic becomes crucial. With the development of Intelligent Transport Systems (ITS) in smart city, public bike sharing system has been considered as an urban transportation system that can collect data from mobile devices. In such network, the biggest challenge for sensor nodes is to forward data to sinks in an energy efficient way because of the following limitations: limited energy resources, limited storage capacity and limited bandwidth. Data aggregation is a key mechanism to save energy consumption and network capacity. It can be defined as an approach to combine data of various sensors into a single packet, thus reducing sensor communication costs and achieving a longer network lifetime. The main contribution of this paper is to introduce an efficient, "Internet of Bikes", IoB-DTN routing protocol based on data aggregation being applied to mobile network IoT devices running a data collection application on urban bike sharing system based sensor network. We propose three variants of IoB-DTN: IoB based on spatial aggregation (IoB-SA), IoB based on temporal aggregation (IoB-TA) and IoB based on spatio-temporal aggregation (IoB-STA). We compare the three variants with the multi-hop IoB-DTN protocol without aggregation and the low-power long-range technology, LoRa type. Comparison results verify that the three variants of IoB-DTN based on data aggregation improve the delivery rate, energy consumption and throughput.
Information technologies offer an excellent framework to integrate the Full Electric Vehicle (FEV) in the future European city ecosystem. In this paper we present a model of the mobility in a city in order to allow FEV to interoperate with the smart grid and a way to design an infrastructure to efficiently control and manage the energy availability and supply in the network of charge stations in the city.
The design of routing protocols in vehicular ad hoc networks (VANETs) is fundamental to achieve a high packets' delivery ratio. Routing protocols whose operation considers the nodes' positions use updated routing information according to the reception frequency of hello messages. The routing information can help to improve the routing operation by including several metrics such as vehicle's trajectory, vehicle's density, percentage of packet losses, among others. In addition, there is a trade-off between the beaconing frequency and the overhead injected to the network: a high frequency provides a better accuracy on selecting the best forwarding node to route a packet but at the same time the overhead is increased. However, a low frequency will provide less accuracy on selecting the best forwarding candidate node but the overhead injected to the network will be decreased. In this paper, we have designed an efficient method to improve the accuracy of the nodes' position used to select the next forwarding node without any modification on the beaconing frequency value. Our approach improves the operation of the routing protocol used in our performance evaluation. Simulations show the benefits of our proposal, increasing the accuracy of the nodes' selection and maintaining the same level of overhead, without the necessity to increase the beacon frequency.
A realistic simulation scenario is instrumental for the study of transportation problems and the possible solutions that Intelligent Transportation Systems (ITS) can provide such as smart traffic lights, warning notification. In this paper we focus on providing a automatic traffic flow monitor, named Vehicular Traffic Monitor~(VTM), to extract information from GoogleMaps traffic layer. Our monitor bases on image processing and the relationship between geographical coordinates to pixel position and does not needs any interface to process vehicular flow information. VTM can be used to obtain traffic flow logs, which are the base to generate realistic, synthetic vehicular traffic traces, specially for areas where those traces are not available. Moreover, we show the traffic flow evolution along a specific day (Father's day) can vary from usual behavior, and therefore our tool is useful to obtain information of such cases.
Wireless mobile communication in vehicular networks is essential for the content delivery of road safety and infotainment services to drivers. However, the vehicles' high mobility and topology changes affect the performance of traditional mobility management protocols over VANETs. Therefore, an efficient mobility management solution that mitigates the challenges of vehicles' mobility is needed. In this paper, we present a predictive hierarchical handover protocol for mobile IP in vehicular networks. We combine the stochastic probability analysis of a hidden Markov model, and the vehicles' movement projection to predict the next handoff. We evaluate the performance of our protocol against different mobile handover protocols using the network simulator NS-2, and various mobility traces. Furthermore, we assess the impact of the different type of observations on the prediction model. Our results showed that our predictive module outperforms all other handover protocols in reducing the handover latency and packet loss.
Optimization of the association between wireless stations and access points (APs) has shown its effectiveness to improve the overall performance of wireless LAN. Most of the previous works do not consider the latest amendments of the IEEE 802.11 standard. The main challenges are to propose models that take into account recent enhancements such as spatial multiplexing (MIMO) at the physical layer and frame aggregation mechanism at the MAC layer. To assess these new features, we derive an association optimization approach based on a new metric, named Hypothetical Busy Time Fraction (H-BTF), that combines the classical Busy Time Fraction (BTF) and the frame aggregation mechanism. This metric is based on local measurements like throughput demand and frame error rate for each station. The model estimates the H-BTF of each AP for any configuration and is thus able to predict H-BTF for other association scheme. Association is then optimized to minimize the load of the busiest APs. This load balancing between APs aims to satisfy stations with regard to their throughput demands. Numerical evaluations performed with the network simulator ns-3 have shown the accuracy of the proposed approach for a large set of scenarios and a significant benefit for the stations in terms of throughput and satisfaction.
This paper describes a modeling based on Generalized Stochastic Petri Nets (GSPN) to analyze the performance of a network probing node in terms of throughput. The probing node is part of a distributed monitoring system. In this environment, the use of multiprocessor and multicore systems, as well as the parallelization of applications, is aimed at improving the node performance. Petri nets allow not only to represent the parallelization feature, but also to include the main events identified in the system: the packet arrival and a two-stage processing. The two-stage processing consists of a first stage in which packet capturing functionalities are performed, and a second stage in which a deeper packet treatment is performed. In addition, the Petri net model can reproduce a shared buffer control mechanism to ensure the integrity of the data. After detailing all the model components, the verification and validation of the model are done by using a simulation tool. With this model, it is expected to estimate the efficiency of the probing node early in the design and development stages.
In the management of transport and logistics, which includes the delivery, movement and collection of goods through roads, ports and airports, participate, in general, many different actors. The most critical aspects of supply chain systems include time, space and interdependencies. Besides, there are several security challenges that can be caused both by unintentional and intentional errors. With all this in mind, this work proposes the combination of technologies such as RFID, GPS, WiFi Direct and LTE/3G to automate product authentication and merchandise tracking, reducing the negative effects caused either by mismanagement or attacks against the process of the supply chain. In this way, this work proposes a ubiquitous management scheme for the monitoring through the cloud of freight and logistics systems, including demand management, customization and automatic replenishment of out-of-stock goods. The proposal implies an improvement in the efficiency of the systems, which can be quantified in a reduction of time and cost in the inventory and distribution processes, and in a greater facility for the detection of counterfeit versions of branded articles. In addition, it can be used to create safer and more efficient schemes that help companies and organizations to improve the quality of the service and the traceability of the transported goods.
In this paper, a collision avoidance system is presented to detect red light running and warn nearby vehicles and pedestrians in real time in order to try to prevent possible accidents. No complex infrastructure-based solution such as those based on radars or cameras is here required. Instead, a ubiquitous solution based on smartphones carried by drivers and pedestrians is proposed so that it is the device inside the vehicle violating a traffic light, the one that self-reports the offence in order to generate alerts and warn nearby vehicles and pedestrians to prevent accidents. The proposal could also be used by road authorities to collect data on traffic lights that are most frequently violated in order to define an action plan to investigate causes and search solutions. It includes a classifier for learning and estimating driver behaviour based on collected data, which is used to predict whether he/she is about to run a red light or detect whether that has already happened. In the first case, the system broadcasts warnings directly to close vehicles and pedestrians through Wi-Fi, while in the second case, the proposal warns vehicles and pedestrians in the neighbourhood through a server. The proposal also involves the use of cryptographic schemes to protect authenticity and integrity of messages sent from traffic lights, smartphones and servers, and privacy and anonymity to promote the use of the system. A beta version with some parts of the proposal has been implemented and the obtained results are promising.
We study local algorithms for sensor selection, in which each sensor in a network uses information from nearby sensors alone to decide if it should be selected to predict the data of non-selected sensors. Our goal is to show how the prediction quality can be improved by increasing the level of knowledge available to each sensor. We specifically study this for a graph model of the network, in which prediction quality is defined by virtual links between sensors. Each node knows the links along all paths of fixed length extending outward from itself. The maximum path length increases with the level of knowledge. We designed algorithms for the first few levels and evaluated them on randomly generated graphs and real datasets, determining the optimal parameters for each algorithm and comparing them to baseline global strategies. Our results show that just knowing the links to immediate neighbors is enough to be as good as a simple global greedy algorithm, and increasing the knowledge improves the selection quality.
We present an empirical study on network dynamics in an outdoor heterogeneous multi-hop WSN deployment for long-term environmental monitoring. We analyze the network dynamics in three aspects, the link level characteristics, the routing level characteristics, and the overall temporal characteristics. Our analysis provides insights including: (1) asymmetric links are the majority in heterogeneous networks which are mainly caused by the hardware heterogeneity; and (2) routing protocol plays an essential role in generating routing dynamics in addition to wireless link dynamics. We also create/compose benchmark profiles based on the observations of network testbed dynamics obtained during a long-term operation. The benchmark profiles include the link information between heterogeneous hardware platforms and the complete routing topological information.
The sparse basis of signals plays a key role in signals processing of wireless sensor networks (WSNs). However, the existing sparse bases, such as principal component analysis (PCA) and discrete cosine transform (DCT), do not support a good recovery effect in WSNs. In this paper, the general K-SVD (K-Means Singular Value Decomposition) is optimized and a new adaptive overcomplete dictionary (K-SVD-DCT) is constructed by extracting features of distributed WSN signals. First of all, we normalize the data and select the DCT matrix as the initial training dictionary D of the K-SVD algorithm, and then use the orthogonal matching pursuit (OMP) method to carry out sparse decomposition on signals, obtaining the sparse representation matrix. Then the dictionary atom is upgraded by iterating D. Eventually, K-SVD-DCT for sensor network signals' sparse representation is obtained after multiple iterations. We evaluate the performances of overcomplete dictionaries constructed by three initial training dictionaries. The experimental results show that the recovery errors of using the K-SVD-DCT are smaller than that of the PCA basis and are similar to that of the DCT basis. However, the successful recovery rate (8.0%) of the DCT basis is much lower than that of the K-SVD-DCT (82%).
Energy efficiency is of paramount importance in designing lowpower wireless sensor nodes. Approximate computing is a new circuit-level technique for reducing power consumption. However, the gain in power by applying this technique is achieved at the cost of computational errors. The impact of such inaccuracies in the circuit level of a radio transceiver chip on the performance of Wireless Sensor Networks (WSNs) has not yet been explored. The applicability of such low-power chip design techniques depends on the overall energy gain and their impact on the network performance. In this paper, we analyze various inaccuracy fields in a radio chip, and quantify their impact on the network performance, in terms of packet latency, goodput, and energy per bit. The analysis is supported by extensive network simulations. The outcome can be used to investigate in which WSN application scenarios such power reduction techniques at circuit level can be applied, given the network performance and energy consumption requirements.