Detecting sedentary behavior has increasing attention due to its significant health implications. However, distinguishing these low-intensity activities in federated learning scenarios is notably more complex than general human activity recognition. This complexity comes from heterogeneous feature distributions that can arise even for the same labeled activity, e.g., running and playing soccer, which may exhibit different sensor patterns despite both representing high-intensity activities. This paper proposes a robust personalized federated learning approach for sedentary behavior classification under adversarial conditions. Our method leverages ordinal pattern descriptors to extract meaningful symbolic representations from wearable sensor time series, then applies a meta-learning framework with Siamese Neural Networks to rapidly adapt across clients. Next, a reputation mechanism further safeguards the global model by penalizing malicious updates. Experiments on multiple public datasets show that our method achieves high F1-scores compared to baselines, affirming its ability to maintain robust performance in privacy-sensitive and adversarial environments.

This paper introduces a novel hierarchical federated learning model, Sedentary-SMELL, for classifying sedentary behavior using wearable device data. Our methodology involves transforming sensor data into Ordinal Patterns (OP) for efficient representation, training a federated autoencoder to capture standard features, and clustering users based on similar activity patterns. We employ meta-learning within clusters for enhanced pattern comparison and conclude with personalized model finetuning, adapting to individual user variations for accurate sedentary detection. Extensive testing on various datasets, including BaSA and Har UML 20, demonstrates the model’s superiority over traditional personalized methods, achieving remarkable F1scores of 0.9958 and 0.9124, respectively. Integrating a personalizing step further refines the model, tailoring it to individual user characteristics while retaining the core structure learned from meta-learning, surpassing the median performance of centralized models across all datasets.

Characterizing and monitoring patient activities through time series data is critical for identifying lifestyle patterns that may impact health outcomes. Sedentary behavior is a significant concern due to its association with various health risks. This study introduces a lightweight supervised classifier for healthcare applications based on ordinal pattern (OP) transformation to detect sedentary behavior in federated learning (FL) scenarios. Our hypothesis is grounded on the idea that sedentary behavior exhibits distinct dynamics compared to other activities, and information descriptors derived from the transformation of OPs effectively capture these differences. Next, we proceed with the FL training. We train a neural network (NN)-based encoder locally and send the local models to a server. The FL process updates the encoder weights based on the encoded representations of the clients’ data, enabling the model to learn from different participants. Finally, we personalize the model for the specific task of classifying sedentary behavior. Our approach utilizes a meta-learning framework, incorporating a Siamese NN to learn a similarity space. We fine-tune the model in this step by further training the last NN layer. This fine-tuning allows the model to adapt and specialize in accurately classifying sedentary behavior. We carry out a comprehensive analysis to support our hypothesis. We also extensively validated our proposal by comparing it with other methods over five different data sets. We obtain the best results using a smaller machine learning model compared with the best approaches in the literature. Specifically, our model has 78.73% times fewer parameters and consumes 48.67% times less energy than the best result in the literature.

The continuous emergence of new applications for Internet-connected road vehicles is imposing unprecedented re-source demand. Motivated by the incorporation of ever more resources into vehicles, this is a trend that, on the downside, is causing vehicular networks to become increasingly more challenging to manage. Departing from the proposition that computing capabilities can help overcome resource allocation problems in vehicular clouds (VCs), in this paper, we formulate ALTAIC, a coalition game to maximize resource utilization while dynamically load-balancing the usage among the VCs. First, we define a Shapley value-based strategy to determine the order in which the tasks are allocated. Then, with the marginal contribution of each task calculated, we employ a simple queue to allocate the tasks in VCs using these values. Finally, we conduct a comparative performance analysis of ALTAIC and relevant approaches. Simulation results show that the proposed solution allocates more tasks than the others and reduces 27.12% the load average of the VCs.

In this work, we overview a novel strategy that can support the decision making of systems dealing with combinatorial problems. We are particularly interested in supporting decision making in nearly real time systems. To target this goal, the proposed strategy must have a configurable time threshold to output the solution. Obviously, the solution presented will not be the optimal one because we are dealing with NP-complete problems. However, the evolution of this research may lead to an interesting class of solutions specifically designed for the real time scenario, where machines have to learn fast how to solve a problem, and take the appropriate measures to avoid a catastrophic situation (such as car crashes, course corrections, and general automation systems). The initial experiments show interesting results, with high gains when compared to similar approaches in the literature.

Traffic jams are an essential and continuous challenge in our cities, responsible for socioeconomic and environmental concerns and an ambitious traffic jams management agenda is urgent. The distributed solutions in the literature for Traffic Management Systems (TMS) are heavily based on beacon messages or proactive communication protocols to share vehicular traffic information among vehicles. Thus, these solutions are not scalable when the number of vehicles increases in the network — when there are traffic jams. To overcome these problems, we propose a new VANET-based traffic management system named CoNeCT: Predictive Congestion Control based on Collaborative Information Sharing for Vehicular Ad hoc Networks. CoNeCT's primary goal is to support vehicles' collaboration in analyzing, predicting, and managing congestion. The proposed system was designed to decrease the number of messages by using a novel road segment load assessment that improves traffic flow classification. Vehicles aware of traffic conditions share it with their neighbors, and they can also request traffic views whenever necessary. Additionally, vehicles can detect significant traffic variations and predict future traffic conditions to improve roads' overall traffic conditions, mitigating the congestion before it arises. Results obtained from an extensive performance analysis show CoNeCT's ability to reduce traffic congestion with a low impact on the wireless communication medium, outperforming the state-of-art systems.

As we move more deeply into information-oriented services and systems, we clearly observe the importance and impact of smart and connected vehicles for urban computing. New Cloud-enabled paradigms have boosted information and service sharing. However, such paradigms rely heavily on the underlying communication layer, inheriting the challenges originated from the high mobility of vehicles. Several works have been devised to cope with highly dynamic vehicular environments in support of effective resource management and allocation, which we discuss in the paper. Moreover, we propose a Fog paradigm solution to resource allocation using a hierarchical method in vehicular clouds. Our method is based on the Multiplicative Analytic Hierarchy Process (MAHP) proposed by Lootsma. MAHP is a branch of another method called Analytic Hierarchy Process proposed by Saaty. Therefore, we used MAHP in the decision-making of the resource allocation process using a Fog paradigm to select the best Fog to allocate certain services. We evaluated the proposed solution comparing to three other decision methods, GREEDY, RANDOM, and RELIABLE. The proposed Fog-oriented Hierarchical Resource Allocation Policy in Vehicular Clouds (FRACTAL) performed better than the other decision methods, fulfilling more services and consequently denying fewer services.

Assuring an efficient Quality of Service (QoS) for heterogeneous Internet of Things services is a challenge, mainly due to spectrum scarcity and bandwidth limitations on the radio access network. To solve these problems, efficient management of airtime per station (STA) is necessary. In this work, we propose a scheduler to perform dynamic network slicing in IEEE 802.11ah Networks. The proposed scheduler is based on virtualization technologies to assure QoS restrictions per slice and can be deployed in the Access Point (AP) or in a virtual machine connected to the AP. Network metrics are used to verify QoS violations per slice over time. Once a QoS restriction is detected, the scheduler performs a reallocation of resources re-configuring the Restricted Access Windows parameters that compose a slice, adjusting airtime per STA. To evaluate the proposed scheduler, we consider a dynamic policy. Simulation results in a smart city scenario show that the dynamic policy is able to scale the network and satisfies QoS slice requirements.

The growth in the number of traffic accidents has become a cause for concern in urban centers. As a result of the increase in population in large cities and the number of vehicles, the consequences of accidents and congestion can be even more significant, considering the impacts on the economy, environment and people's quality of life. Therefore, aiming to minimize these impacts, we present ALIVE, a distributed and Vanet-based solution that reduces congestion caused by different sources and, especially, from accident sources, contributing to efficient urban mobility in smart cities. The solution performs the detection of accidents and the dissemination of warning messages in multiple hops. Besides, the system can share the road traffic information with the nearby streets to improve traffic efficiency. We evaluated the proposed solution with PANDORA and NRR Traffic Management solutions. Simulation results indicate that the proposed solution reduces the average travel time, time lost, and the number of transmitted messages.

Road capacity infrastructure and temporary interruptions in trips constitute the main reasons behind the traffic jam phenomenon. City urbanization and growth further intensify these two reasons through the increase of work area and the demand for mobility. In such a scenario, several issues can emerge, such as higher mobility costs, more frequent traffic jams, more significant environmental damage, reduced quality of life, and more pollution. Therefore, this work presents a Proactive and Reactive Approach for Urban Traffic Management in Vehicular Networks, RIDER, to minimize traffic congestion. RIDER is a fully-distributed protocol that can assume proactive and reactive behaviors for sharing traffic condition information. Vehicles with traffic condition information can organize them-selves to improve traffic flow and reduce traffic congestion. In the proposed solution, vehicles monitor the road traffic condition and proactively share this information when needed, considering adaptive multi-hop communication. If vehicles do not have nearby road traffic information, they executed a reactive traffic information discovery. RIDER was evaluated and compared to previous works, regarding the number of transmitted messages, packet collisions, and traffic congestion metrics.

The economic and environmental impacts caused by traffic congestion are increasing. Improvements in the cities road infrastructure for minimizing these impacts are pricey and do not happen immediately. Thus, in order to improve vehicular traffic flow in dense urban centers, we present REACT, a traffic management system to minimize vehicle congestion in Smart Cities. REACT is a traffic management system based on Vehicular communication, and it is divided into Request and Response phases. The Request phase allows vehicles to request traffic information from neighbor road segments. The Response supports vehicles to respond to the request with current road traffic information. The performance evaluation shows the ability of our solution to reduce traffic jams with a low communication overhead.

Intelligent Transport Systems rely extensively on the proper management of vehicular resources, as well as the underlying vehicular communication. Services and applications are made accessible through the communication of vehicles, which is expected to happen without any interruption; however, the high mobility of vehicles is detrimental towards their connectivity and communication stability. In this highly dynamic and heterogeneous vehicular scenario, we assume the existence of multiple communication interfaces and high movement speed of nodes. Thus, we propose the development of a flow mobility management policy. The policy is devised following a decentralized design, being flexible and capable of providing transparent flow management to users while maintaining continuous service access. We conducted experimental simulations for evaluating the proposed architecture, as well as making performance comparisons with three related flow management works. The results showed that the proposed policy reduces the delay of information exchange to approximately 50 ms, decreases handover time (0.5s), and maximizes the input of information around 95%.

Traffic congestion in large cities became more intense considering the last years. Basically, this growth is attributed to the wide use of a single mode of transport due to the lack of alternatives capable of efficiently supplying urban traffic demand. In this sense, in economic terms, it is estimated that billions of dollars are wasted every year due to the extra expenses with fuels and maintenance caused by traffic. In order to minimize the economic and environmental damages caused by congestion, this work presents FIRE-NRD: A fully-distributed Traffic Management System for Next Road Decision. In the proposed solution, vehicles, while moving, are able to analyze and share a study of the traffic flow and thus provide sufficient knowledge in order to resolve "the next road decision", i.e., the next road choice to decrease its travel time. FIRE-NRD is based only on local data about traffic information and totally collaborative, where neighbor vehicles share their knowledge. FIRE-NRD is compared to literature solutions and present better results considering network and traffic metrics, such as number of messages and average travel time.

Recently, Network Slicing in Radio Access Technologies (RATs) has been proposed as an approach to divide the wireless network infrastructure into isolated logical slices which are defined following their requirements and features in a service-driven way. To the best of our knowledge, the application of Network Slicing has not been explored in IEEE 802.11ah Networks. In this work, we propose a Virtual Network Slicing Broker (VNSB), a Virtual Network Function (VNF) instantiated inside a Software Defined Network (SDN) controller which communicates with an IEEE 802.11ah network Access Point (AP) via a southbound Application Program Interface (API). Based on information obtained by the northbound API, the slicing broker gets the information contained in the slicing templates, which describes the services features and respective Quality of Service (QoS) restrictions. With these data, the slicing broker makes logical slices in the context of the Restricted Access Windows (RAW) which are periodically sent by the AP to the stations (STA) via the Raw Parameter Set (RPS). Since there is no hardware with the IEEE 802.11ah standard available on the market, we validate the proposed solution through extensive simulations in a typical smart city scenario. Results showed the broker capacity to build and manage logical slices per service, respecting the QoS restrictions of each offered service. Moreover, the proposed slicing broker makes use of available resources of neighbor slices reducing delay, packet loss, and efficiently maximizing throughput per slice.

Typically, the electric power companies employ a group of power meter readers to collect data on the customers energy consumption. This task is usually carried out manually, which can lead to high cost and errors, causing financial losses. Some approaches have tried to minimize these problems, using strategies such as discovering the minimal route or relying on vehicles to perform the readings. However, errors in the manual readings can occur and vehicles suffer from congestion and high fuel and maintenance costs. In this work, we go further and propose an architecture to the Automatic Meter Reading (AMR) system using Unmanned Aerial Vehicles (UAV). The main challenge of the solution is to design a robust and lightweight protocol that is capable of dealing with wireless communication collisions. Therefore, the main contribution of this work is the design of a new protocol to ensure wireless communication from UAV to the power meters. We validated and evaluated the architecture in an urban scenario, with results showing a decrease of time and distance when compared to other approaches. We also evaluated the system proposed with Linear Flight Plan, the Ant Colony Optimization and Guided Local Search metaheuristic. Our mechanism attains an improvement of 98% in reducing the message collisions and reducing the energy consumption of the power meters.

Vehicular network applications may be benefited by the use of simultaneous network interfaces to maximize through-put and reducing latency. In order to take advantage of all radio interfaces of the vehicle and to provide a good quality of service for vehicular applications, we have developed an architecture that performs the management of the flow mobility based on some classes of application for vehicle network. Our goal is to minimize the time of handover between the rings of flows in order to meet the minimum requirements of vehicular applications, as well as to maximize the throughput. Simulations have been conducted to analyze the performance of the proposed architecture by comparing it to other previously devised architectures. As a result, the proposed architecture presented a low delivery time of messages, packets with lower loss and lower delay.

Gamma Deployment is a metric for evaluating the distribution of roadside units in vehicular networks in terms of two parameters: a) the inter-contact time between vehicles and the infrastructure, and, b) the share of vehicles that must respect the inter-contact time guarantees. We envision the use of the Gamma Deployment metric when the network designer intends to distribute check-points along the road network in order to collect and disseminate traffic informations through roadside units. Thus, the goal is to locate the roadside units such that ρ percent of vehicles meet roadside units in time intervals less than τ seconds. In this work, we formalize the Gamma Deployment metric by developing an Integer Linear Programming formulation (ILP). Since the ILP is not able to solve large instances, we also develop an heuristic for approximating the optimal solution. We present experiments considering a realistic mobility trace, and our results demonstrate the heuristic incurs in small deviations for small inter-contact time guarantees.

Several Intelligent Transportation Systems (ITS) rely on localization to enable services ranging from comfort to safety applications. Following this same idea, the use of Dedicated Short Range Communications (DSRC) devices based on the IEEE 802.11p standard, and the Vehicular Ad Hoc Networks (VANETs) paradigm, have resulted in the deployment of several protocols, services and applications that need different localization accuracy levels. The most common solution for localization is based on Global Navigation Satellite Systems (GNSS). GNSSs have problems of unavailability in dense urban areas, tunnels and multilevel roads. Moreover, GNSS have errors in the range of 5-15 meters. These unavailability and error problems are not acceptable for several VANET and ITS safety applications. In this work, we investigate and characterize the GNSS problems of error and unavailability based on datasets of real GNSS devices installed in vehicles and develop a Geodesic Dead Reckoning solution to overcome the GNSS unavailability issue.

Network virtualization is considered a key technique for the future internet, working as an alternative for the "internet ossification" problem. Virtualization technology is considered a new paradigm of networks, allowing multiple virtual networks to co-exist in a common physical substrate independently. In this context, the Virtual Network Embedding problem arises, consisting of mapping virtual nodes and links in the physical network components while respecting their capabilities. The problem belongs to the NP-hard class, and aiming to provide good solutions in feasible time, a metaheuristic approach is developed. When fault tolerance is taken into account, an extension of the problem leads to the resilient network embedding problem. Experimental results demonstrate the efficiency of the proposed method in comparison to literature methods and the trade off to provide resilience.

In this work, we study the design of a Wireless Sensor Network based on the Small world models. By modeling a sensor network with small world features, it is possible to decrease the average path length to interconnect the sink and sensor nodes. The goal of this work is to analysis the Newman & Watts and Kleinberg small world models in wireless sensor networks. The simulation results showed that both models are able to create a sensor network with small world features, however, the Newman & Watts model has better results regarding the path length, clustering coefficient and data communication latency. On the other hand, the Kleinberg model reduces more the energy consumption during data communication.

This work proposes an intelligent decision system for a residential infrastructure based on wireless sensors and actuator networks, called ResiDI. ResiDI is equipped with battery-powered nodes to ensure that they are deployable anywhere in the house without the need for wiring, drilling or any pre-existing infrastructure. The key intelligence of ResiDI is distributed in the decider nodes, which are able to make decisions locally without the need to send traffic from the sensor nodes to the sink. The network intelligence core is based on a neural network that seeks to improve the accuracy of the decision-making, together with a temporal correlation mechanism that is targeted at reducing the energy consumption. When compared with an approach adopted in the literature, the results show that ResiDI is efficient in different scenarios in all evaluations performed.

In this research, we propose a protocol to create different logical topologies that consider the same physical network topology for the data routing problem in Heterogeneous Sensor Networks (HSNs). The HSN in question has two types of sensor nodes, called L-Sensors (sensor with Low hardware capabilities) and H-Sensors (sensors with High hardware capabilities). The proposed protocol creates different topologies, each designed to meet different application requirements, and use a fraction of the links between H-sensors. In addition, each topology has a tradeoff between latency and energy consumption during data communication. The simulation results show that our routing algorithm based on different topologies reduces energy consumption compared to a literature protocol.

Network design is a key challenge in order to supply a large set of requirements from demanding customers which are looking for high quality telecommunications services. Such requirements include bandwidth capacity, fault tolerance, security, low levels of delay, among others, so that the fulfilment of these requirements will directly impact on quality of service and customer satisfaction. Considering the current network scenario, the existence of different classes of customers (according to their requirements) and the availability of many technologies (with different capacities and consequently proportional costs), it is possible to design topologies to meet the demanding requirements but also optimize costs implied by such infrastructure. In this paper, we propose two heuristics for the problem of resilient heterogeneous network design with quality of service. Computational results show the efficiency of the proposed methods compared with another method from the literature.

Typically, electric power companies employs a group of employees known as meter readers to collect data of energy consumption of customers. To overcome the challenges and limitations of the literature approaches, we propose an automatic meter reading system based on Unmanned Aerial Vehicle and Wireless Sensors Networks. In our approach, each electric meter has one sensor node device with wireless communication capability and the Unmanned Aerial Vehicle flies the field in a predefined way to collect data from the wireless sensor nodes without having to visit each electrical meter. Simulation results show that our approach reduces the distance to perform the readings compared to literature solutions. We also present a use case analysis considering real parameters for meter readings.

In this paper, we consider the Grooming, Routing and Wavelength Assignment (GRWA) problem with protection and Quality of Service (QoS) for optical mesh networks. Assuming that traffic demands can vary from low rate to very high demands, grooming several requests into the same wavelength allows a more efficient use of the network capacity. Besides, the need for fault tolerance and QoS became evident in today's networks. Our objective is to minimize the total number of wavelengths used. A mathematical formulation of the problem is presented in this study along with a column generation-based heuristic. We evaluate the performance of the proposed approach and analyze the role played by the protection and QoS constraints. Our results show that the proposed algorithm is able to find solutions close to optimality for real-world network instances.

Localization and synchronization are fundamental services in Wireless Sensor Networks (WSNs), since it is often required to know the position and the global time of sensor nodes to relate a given event detection to a specific location and time. However, the localization and synchronization tasks are often performed after the sensor nodes' deployment. Since manual configuration of sensor nodes is an impractical activity, it is necessary to rely on specialized algorithms to solve the localization and synchronization problems. With this in mind, in this work we propose a joint solution for the 3D localization and time synchronization in WSNs using an unmanned aerial vehicle (UAV). A UAV equipped with a GPS flies over the sensor field area broadcasting its geographical position. Therefore, sensor nodes are able to estimate their own geographical position and global time without the need of equipping them with a GPS device. By means of simulations, we show that our proposed joint solution leads to smaller time-synchronization and localization errors when compared to existing solutions.

A wireless sensor network (WSN) is designed to perform event detection, data collection, and reporting such data to a monitoring station. In many cases, it is necessary to know the location of sensor nodes to relate the detection of the event at a specific location. However, the geographical location of the sensor nodes in most applications can only be set after their deposition in the area of interest. Therefore, for the sensor nodes to know their location, it is necessary to use specific algorithms to solve the problem of discovering the geographical position of sensor nodes. This work addresses the problem of 3D localization in WSNs using an Unmanned Aerial Vehicle (UAV). The UAV is equipped with GPS and it flies over the monitoring area broadcasting its geographical position. Thus, the sensor nodes are able to estimate their geographical position without being equipped with GPS receiver. Simulation results show that using an UAV leads to a smaller error in the calculation of geographic location when compared to solutions presented in the literature.

In this work we propose a framework based on the small world features to design Heterogeneous Sensor Network (HSN) topologies with QoS. The framework creates three different topologies and each topology has its goals related to the latency and energy consumption during data communication. We also propose a routing algorithm that uses the created topologies in order to provide QoS in HSN. Simulation results showed that the three topologies can provide different levels of QoS that can be used in many sensor network applications. We also compare our framework to the literature topology to provide QoS in HSN and our framework presents better tradeoff between energy consumption and latency during data communication.

The emergence of WDM technology has provided the explosive traffic growth on telecommunications networks. Although in future networks a request may require more than a single wavelength, in general, in today's networks, the size of a single request tends to be much smaller than the capacity of a wavelength channel. Thus, the need for traffic grooming became evident. In this paper, we present formulations based on network flows and column generation for the problem of routing and wavelength assignment (RWA) with traffic grooming, protection and quality of service on optical networks in order to minimize the number of wavelengths used. This problem is NP-complete and exact approaches are commonly practicable only in small instances. In this study, the employment of advanced techniques such as column generation and branch-and-price led to solve the problem for instances of real-world network size.

Data aggregation is one of the main methods to conserve energy in wireless sensor networks (WSN). Redundant data can be aggregated at intermediate nodes of a WSN reducing the number of messages exchanged and, consequently, reducing communication costs. Most data aggregation protocols are generally based on a static routing scheme. Although those protocols can save energy by eliminating data redundancy, in dynamic scenarios, they can incur in high overhead to reconstruct the routing tree. In this work we consider the problem of constructing a dynamic and scalable structure for data aggregation in WSNs. To tackle these challenges we propose a novel routing protocol called Dynamic and Scalable Tree (DST), which can adapt to different scenarios without incurring the overhead of the other methods. DST maximizes the number of overlapping routes and selects routes with the highest aggregation rate. DST was extensively compared with two solutions reported in the literature regarding communication costs, aggregation rate efficiency and quality of the routing tree. Simulation results show that the routing tree built by DST provides the best efficiency compared with other algorithms outperforming them for different scenarios in all evaluations performed.

A typical Wireless Sensor Network (WSN) assumes a homogeneous set of nodes in terms of capabilities. However, this kind of network suffer from poor fundamental limits of latency during the data communication. Another model of WSN assumes a heterogeneous set of nodes with different capabilities (especially in terms of communication range and energy reserves) called Heterogeneous Sensor Networks (HSNs). In this work, we propose a tree-based approach based on small world concepts to design HSNs. The proposed model introduces an adjustable search space in order to find H-sensors to create shortcuts between them. The endpoints of these shortcuts are nodes with more powerful communication range and energy reserves to support the long communication range. The model was designed in such a way that the created shortcuts among the powerful nodes create a connected topology among them. As a consequence of this, it is not necessary that the nodes with lower capabilities forward messages as a bridge to interconnect the shortcuts. Simulation results showed that with just a few powerful nodes, a wireless sensor network can be tuned into a HSN with small world features. Also, when the shortcut addition creates a connected topology among the H-sensors, the data communication latency is significantly reduced.

Dense sensor networks are typically deployed for fine-grain monitoring in a wide range of applications. Due to this high density of nodes, it is very likely that both spatially correlated information and redundant data be detected by several nearby nodes, which can be exploited to save energy by these nodes that are sensing an event. In this work, we propose an Energy-Aware Spatial Correlation mechanism (ESC) based on correlation regions to perform efficient data collection. The sensed information of a region is forwarded to the sink by only one node inside that region. Also, the area of a correlation region can be changed dynamically by the sink node to achieve the required accuracy of the sensed information. Simulation results show that using ESC, an event can be sensed with 97% of accuracy, and 75% of the nodes' residual energy can be saved within the phenomena area when compared with the classical approach for data collection.

One of the main applications in Wireless Sensor Networks (WSNs) is the accurate monitoring of extensive areas. However, due to the high density of nodes, redundant and correlated data are usually sensed by nearby nodes over time. Since saving energy is a key aspect of these networks, it is important to take advantage of these correlations to decrease communication and data exchange. However, current proposals usually results in high delays and outdated data arriving at the sink node. In this work, we go further and fully exploit both spatial and temporal correlations to perform near real-time data collection in WSNs. Also, in our proposal, called Efficient Data Collection Aware of Space-Time Correlation (EAST), obtained results clearly indicate that an event can be sensed with a high accuracy of more than 99.7% while still saving the residual energy of the nodes in more than 14 times when compared to the accurate data collection strategy.

In this work, we propose a small world model to design Heterogeneous Sensor Networks (HSNs). The proposed model takes into account the communication pattern of this network to create shortcuts directed to the monitoring node, decreasing data communication latency. The endpoints of these shortcuts are nodes with more powerful communication range and energy reserves to support the long communication range. However, this kind of communication causes a high interference in the wireless channel. For this, the proposed model uses the multi-interface and multi-channel capability of the MAC layer to reduce the number of collisions during data communication. Simulation results showed that with just a few powerful nodes, a wireless sensor network can be tuned into a HSN with small world features. Also, when the shortcuts are directed to the monitoring node and assigned to a different wireless channel, the number of collisions and the data communication latency are reduced.

In wireless sensor networks, there are some network functions and applications that need to know the local time of a node in order to timestamp a sensed data. In general, it may be unfeasible to have a synchronization hardware in each sensor. In this work, we propose a heterogeneous wireless sensor network model based on small world concepts to improve synchronization algorithms for wireless sensor networks. This improvement has two goals: (i) reduce the synchronization error for large wireless sensor networks, and (ii) increase the resilience of synchronization algorithms in the presence of node failures. Therefore, we modified and evaluated the Flooding Time Synchronization Protocol in the proposed model. Simulation results show that the use of small world based models in wireless sensor networks can significantly reduce the synchronization error and also increase the resilience in the synchronization problem.

Data aggregation plays an important role in energy constrained wireless sensor networks (WSN). Redundant data can be aggregated at intermediate nodes of a WSN reducing the number of messages exchanged and consequently reducing communication costs. In this work we consider the problem of constructing a dynamic and scalable structure for data aggregation in WSN. Although there are many proposed solutions to data aggregation in WSN, most of them build the data aggregation structure based on the order in which events occur. This kind of structure leads to low quality routing trees and does not address the load balancing problem, since the same tree is used throughout the network life. To tackle these challenges we propose a novel routing protocol called Dynamic and Scalable Tree (DST), which reduces the number of messages necessary to set up a routing tree, maximizes the number of overlapping routes, and selects routes with the highest aggregation rate. The routing tree created by DST does not depend on the order of events and is not held fixed along the occurrence of events. DST was extensively compared with two solutions reported in the literature regarding communication costs, aggregation rate and quality of the routing tree. Results show that the routing tree built by DST provides the best aggregation quality compared with other algorithms outperforming them for different scenarios in all evaluations performed.

In this work, we propose an on-line model 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 a way that the communication between the sink and the sensor nodes is optimized. The end-points of these shortcuts are nodes with more powerful hardware, leading to a heterogeneous sensor network. We evaluate the on-line model and show that it presents the same small world features observed in the theoretical model. When the shortcuts are created toward the sink node, with a few 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 original model proposed in the literature.

Reprogramming is an important and challenging problem in wireless sensor networks because it is often necessary to in-network sensor processing. Thus, over-the-air programming is a fundamental service that relies upon reliable broadcast for efficient distribution. In this work we use small world features to improve the over-the-air programming. The small world based protocol takes into account the communication workflow of sensor networks to create shortcuts toward the sink, thus improving the reprogramming process. The endpoints of these shortcuts are nodes with more powerful hardware, resulting in a heterogeneous wireless sensor network. We then evaluate the behavior of the small world based protocol regarding the number of transmitted messages, energy consumption and time to reconfigure the network.

Data communication may have a strong impact on the design of a wireless sensor network (WSN) since the energy cost related to the data transmission is typically much higher than the energy cost to perform data processing. Typically, data communication in a WSN tends to be different from other "traditional" data networks such as the Internet. In a WSN, there is a special node called sink that is either the origin or the destination of a message whereas, in the other networks, data communication happens between arbitrary communicating entities. In this scenario, the latency and the energy consumption during communication between the sink node and the other nodes can be optimized using Heterogeneous Sensor Networks (HSNs) created by applying the small world concepts. In order to generate a network with small world features, one should add a small number of long-range links, called shortcuts, whose endpoints are equipped with more powerful hardware, mainly the energy resources. In this work, we propose and evaluate two small world models that can be used in the design of HSNs. In particular, the directed angulation toward the sink node model exhibits the most interesting tradeoff between energy consumption and latency and the sink node as source/destination model presents the best results related to energy consumption and latency in the nodes near to the sink.

In a wireless sensor network, data communication may have a strong impact on its design since the energy cost related to the transmission is typically much higher than the energy cost to perform data processing. Typically, data communication in a WSN tends to be different from other ldquotraditionalrdquo data networks such as the Internet. In a WSN, there is a special node called sink node that is either the origin or the destination of a message whereas in the other networks data communication happens between arbitrary communicating entities. In this scenario, the theory of complex networks is being employed in the design of wireless sensor networks, which have certain non-trivial topological features. One of the most well-known examples of complex networks is small-world network. In this work, we will use the small-world concept as a modeling technique to build efficient data dissemination in a wireless sensor network. We propose and evaluate two small-world models that can be used in the design of a WSN. In particular the Sink Node as Source/Destination model (SSD) exhibits the most interesting tradeoff between energy and latency allowing the design of strict applications that demand a small latency and energy consumption.

A gossip protocol randomly decides the set of nodes that will forward a packet it received. Gossiping was proposed to be used in dynamic topology networks such as Wireless Sensor Networks (WSNs). This work proposes Gossiping using the Energy Map (GEM), a new gossiping-based protocol to perform energy-aware broadcasting in WSNs. The key idea is to change the random selection of neighbors in a way that the selection process uses the energy map. In our protocol, the routing flow is directed to the nodes with the greatest energy reserves, balancing the network energy and preserving nodes localized inside low energy regions to perform sensing tasks.