Video door entry systems installed at scale in residential buildings predominantly rely on unshielded twisted-pair wiring, whose useful bandwidth is limited to a few hundred kHz—a constraint to provide video transmission. Replacing this infrastructure or resorting to high-cost xDSL/PLC modems is economically prohibitive for the vast majority of deployments.

The demand for immersive multimedia experiences has propelled the adoption of 4K 360-degree video streaming. However, delivering such high-resolution content imposes challenges related to bandwidth constraints and computational overhead. Adaptive streaming based on the user Field of View (FoV) offers a solution by providing high-quality video only in the viewed area and reducing the quality of peripheral regions, thereby optimizing bandwidth usage with low impact on user experience. This paper proposes a cloud-edge architecture for live encoding and streaming of 4K 360-degree videos using FoV-based adaptation within the Dynamic Adaptive Streaming over HTTP (DASH) framework. Leveraging cloud-native technologies like Kubernetes and Knative, the system employs a GPU-accelerated serverless architecture triggered by event-driven mechanisms. Experimental results demonstrate that the proposed system meets the latency requirements of live streaming, maintaining end-to-end processing times well below the 10-second threshold. Additionally, the adaptive streaming approach can achieve up to a 77% reduction in bandwidth consumption by adjusting the quality of video tiles based on the user’s FoV.

ABSTRACT Background Modern microservice architectures demand not only modularity, scalability, and maintainability but also adaptation to dynamic requirements. For applications deployed on Kubernetes, sidecar proxies have been used to separate the operational features (such as security, networking, or monitoring) from the application business logic by intercepting traffic to and from microservices or functions in serverless platforms. Existing proxy sidecar implementations such as Envoy offer these operational features as chains of logic that cannot be composed at deployment time. Objective This work introduces K8Sidecar, which leverages the operator pattern to dynamically integrate a chain of proxy sidecar containers into microservices or serverless architectures deployed on top of Kubernetes. Methods The study presents the architecture of the software and describes the Java and Go libraries provided to develop new proxy sidecars. Results We provide illustrative examples and evaluate the impact of the number of injected sidecars on cold‐start and latency and compare them with Envoy. Conclusion Results show that for up to 5 sidecars, the chain‐of‐sidecars approach (especially using the Go library) remains reasonably close to Envoy in terms of both cold start and latency.

Weeds are a major threat to agricultural productivity, as they have a significant influence on crops. Their presence leads to increased production costs, derived mainly from crop wastage, which negatively affects the global agricultural economy. Aware of the seriousness of this problem, the research community is actively exploring technological solutions to help farmers quickly identify and tackle weed infestations. Automatic identification and location of these weeds can help farmers to increase the productivity and efficiency of their crops.

Field index maps using multispectral images are of great importance in decision making in precision agriculture. Being able to develop this type of map in a simple way and using web tools can be of great help to the many agents involved in the agricultural sector. For this reason, it has been decided to develop a web tool, which allows processing multispectral images captured by a DJI Mavic 3M drone, and then calculating various field indices that will be useful for the detection of diseases and/or pests in agriculture. This tool allows obtaining 9 field indices and their visualization through a web browser.

In the digital era, video content dominates network traffic, with HTTP Adaptive Streaming commonly used by streaming services. Recently, novel video encoding schemes based on Deep Neural Networks (DNN) have been proposed. One of the proposals used features extracted from fixed length downscaled video segments. The DNN used as input the features and the desired Video Multimethod Assessment Fusion (VMAF), to estimate adaptively the Constant Rate Factor (CRF) to be applied to the original segment to attain the target VMAF. In this work we analyze the generalization capabilities of this trained DNN when instead of fixed length segments we consider scenes, that have variable duration. We show that the CRF is adapted per scene and per video and the final encoded videos have the requested VMAF. According to our tests, the encoded videos have an average absolute error of 1.34, being less than 2 and therefore not perceived by the end user. Besides, the size of the encoded videos decreases, up to 12% in the best case, compared to those obtained estimating the CRF from fixed length segments.

In the dynamic area of container orchestration, precise and timely metrics collection is essential for optimal resource management. PodInsights, an innovative Kubernetes metrics collector, surpasses existing tools by directly collecting pod CPU and memory usage data from the operating system at millisecond intervals. Deployed as a DaemonSet, PodInsights efficiently monitors selected pods based on configurable labels, offering unparalleled precision in capturing rapid fluctuations in resource consumption. Rigorous testing across different scenarios validates its accuracy, highlighting its potential to improve high-frequency data gathering in Kubernetes environments.

Abstract In recent years, Internet traffic patterns have been changing. Most of the traffic demand by end users is multimedia, in particular, video streaming accounts for over 53%. This demand has led to improved network infrastructures and computing architectures to meet the challenges of delivering these multimedia services while maintaining an adequate quality of experience. Focusing on the preparation and adequacy of multimedia content for broadcasting, Cloud and Edge Computing infrastructures have been and will be crucial to offer high and ultra-high definition multimedia content in live, real-time, or video-on-demand scenarios. For these reasons, this review paper presents a detailed study of research papers related to encoding and transcoding techniques in cloud computing environments. It begins by discussing the evolution of streaming and the importance of the encoding process, with a focus on the latest streaming methods and codecs. Then, it examines the role of cloud systems in multimedia environments and provides details on the cloud infrastructure for media scenarios. After doing a systematic literature review, we have been able to find 49 valid papers that meet the requirements specified in the research questions. Each paper has been analyzed and classified according to several criteria, besides to inspect their relevance. To conclude this review, we have identified and elaborated on several challenges and open research issues associated with the development of video codecs optimized for diverse factors within both cloud and edge architectures. Additionally, we have discussed emerging challenges in designing new cloud/edge architectures aimed at more efficient delivery of media traffic. This involves investigating ways to improve the overall performance, reliability, and resource utilization of architectures that support the transmission of multimedia content over both cloud and edge computing environments ensuring a good quality of experience for the final user.

With the evolution of softwarised industrial infrastructures, there is an increasing need for more sophisticated cyber security solutions that can protect industrial processes from a rapidly evolving landscape of cyber threats. In this context, we present an agent-based approach that provides process monitoring, predictive process behaviour, and process control to give the organisations appropriate situational awareness in relation to cyber security threats, enabling them to re-actively or pro-actively detect attacks and respond to advanced persistent threats and multi-vector attacks. Our architectural solution is based on four agents: Process Inventory Agent (PIA), Process Monitoring Agent (PMA), Process Forecasting Agent (PFA), and the Process Slicing Control Agent (PSCA), which work together to deliver a novel mitigation tool to secure softwarised industrial environments. The architecture has been designed, prototyped, and validated in order to demonstrate the effectiveness of our solution. Experimental results show that the proposed solution can successfully mitigate different attacks in the concerned context.

Nowadays, video content accounts for a large percentage of network traffic. Most streaming services use HTTP Adaptive Streaming by splitting the video in non-overlapping segments and by encoding each video segment independently (possibly with multiple representations to allow adaptation to the varying network conditions). In this work we propose an encoding scheme based on a Deep Neural Network (DNN), to perform a per-title and per-segment adaptive estimation of the encoding parameter to achieve a target video quality of the encoded video. A dataset has been prepared using 1212 segments obtained from 158 videos. The segments have been encoded with 19 Constant Rate Factor (CRF) values using the VP9 encoder, generating a total of 23028 encoded segments, and for each encoded video segment, its Video Multi-Method Assessment Fusion (VMAF) quality has been computed. Besides, from a 240p downscaled version of the segments a feature vector has been obtained, and an analysis of the dependency of the features with the resolution has been carried out. With this dataset a DNN has been trained to estimate the CRF to be applied to each segment to achieve a target VMAF quality. Results show that the trained network is able to provide the CRF value to be applied to each video segment to achieve the desired quality with low computational overhead. To validate the proposal, the network has been used to predict the CRF to encode 1840 two-second segments, not used during the training process, using the VP9 codec at Full High Definition (FHD) resolution, and four target quality values. Results show that the system adapts the CRF to each segment and that the final videos have a mean deviation of 1.84% with respect to the requested VMAF value.

Abstract Nowadays, the majority of Internet traffic is multimedia content. Video streaming services are in high demand by end users and use HTTP Adaptive Streaming (HAS) as transmission protocol. HAS splits the video into non-overlapping chunks and each video chunk can be encoded independently using different representations. Therefore, these encode tasks can be parallelized and Cloud computing can be used for this. However, in the most extended solutions, the infrastructure must be configured and provisioned in advance. Recently, serverless platforms have made posible to deploy functions that can scale from zero to a configurable maximum. This work presents and analyses the behavior of event-driven serverless functions to encode video chunks and to compute, optionally, the quality of the encoded videos. These functions have been implemented using an adapted version of embedded Tomcat to deal with CloudEvents. We have deployed these event-driven serverless pipelines for video coding and quality metrics on an on-premises serverless platform based on Knative on one master node and eight worker nodes. We have tested the scalability and resource consumption of the proposed solution using two video codecs: x264 and AV1, varying the maximum number of replicas and the resources allocated to them (fat and slim function replicas). We have encoded different 4K videos to generate multiple representations per function call and we show how it is possible to create pipelines of serverless media functions. The results of the different tests carried out show the good performance of the serverless functions proposed. The system scales the replicas and distributes the jobs evenly across all the replicas. The overall encoding time is reduced by 18% using slim replicas but fat replicas are more adequate in live video streaming as the encoding time per chunk is reduced. Finally, the results of the pipeline test show an appropriate distribution and chaining among the available replicas of each function type.

Video content represents a high percentage of the traffic on Internet with an increasing number of platforms offering live and on demand video. Most platforms use HTTP Adaptive Streaming (HAS) to deliver their content. In HAS, the provider splits the videos in segments and each segment is offered in multiple representations. The player can dynamically request the appropriate representation for the next segments depending on the varying conditions. This alleviates problems such as initial delay or stalling, thus providing a better quality of experience to users. These segments can have a fixed duration or can be adapted to match scenes up to a maximum duration of typically 10 seconds. In this work, we study the effects in time, quality and size of the encoded videos when downscaling the video to obtain the scenes. The experiments are performed using two codecs: H.264 and VP9, and using 10 videos with 4K resolution with a duration of more than 150 seconds. The videos are encoded using fixed segments and variable segments based on scenes (obtained from the downscaled video). Results show that the use of downscaling to obtain the scenes has a small impact on the final quality, while reduces the total time, the consumption of computational resources, and the size of the encoded video.

Today, more than 85% of Internet traffic has a multimedia component. Video streaming occupies a large part of this percentage mainly because this type of content is provided by the most used applications on the Internet (e.g. Twitch, TikTok, Disney+, YouTube, Netflix, etc.). Most of these platforms use HTTP Adaptive Streaming (HAS) to send this media content to end users in order to ensure a good quality of experience (QoE). But, this QoE should be guaranteed from the video to be transmitted, i.e., the video should have an adequate quality by minimizing the bitrate before transmission. In order to solve this issue, we present a system capable of encoding a video in several resolutions given the desired value of an objective metric. Our system includes the objective metric in the encoding loop in order to maintain the quality in all segments. This system has been tested with three video and five resolutions for each video. Our proposal provides improvements of more than 10% in terms of video size and with similar coding times when compared with a fixed Constant Rate Factor (CRF) encoding. A visual comparison between our proposal and a fixed CRF encoding can be seen at: https://links.uv.es/jgutierr/multiresQ

Lightnings are violent natural phenomena and can generate many expenditures, specially when they strike in urban areas. The identification of the concrete geographic area where they strike is of critical importance for emergency services in order to enhance their effectiveness by doing an intensive coverage of the affected area. To achieve this aim, this paper proposes a design, prototype and validation of a distributed network of Internet of Things (IoT) devices to enable detection and location of lightning strikes. The IoT devices are empowered with lightning detection capabilities and are synchronized with the other devices in the sensor network. All of them cooperate within a network that is able to locate different events thanks to a trilateration algorithm implemented in a big data environment. The designed low cost lightning detection system is based on the AS3935 sensor. This alone device has a limited range of effective detection, but when it is embedded in a IoT mesh network, the accuracy and performance is increased up to good levels, in the order of kilometres. A fully operational IoT network has been deployed and a functional validation and empirical measurements are provided.

Wireless sensor networks (WSN) are groups of small devices that contain a microcontroller in which a large number of sensors can be added. They transmit data and communicate to each other in the ISM band, standard IEEE 802.15.4, exchanging packets using a multi-hop routing. These devices are named motes and are nodes of the WSN. They are very simple and easy to program, powered by batteries of 1.5Volts (AA and AAA). The nodes are autonomous elements that can be deployed implementing any type of network. In a typical deployment the nodes communicate with each other and with a master node or Base Station (BS), which in turn transmits the information to an external server, which collects the environmental and any type of parameters collected by each sensor. WSNs are used in a multitude of military, industrial and civil applications. They use a very low amount of power, and this fact limits the maximum communication distance between motes within the WSN, which is around 50m for civil current motes. The Stochastic Collaborative Beamforming (SCB), was proposed as a way to overcome the low range. in which we take advantage of the synchronization errors of the clocks. In SCB, it is possible to obtain the adequate time delay that permits the interference or sufficient gain in the direction of the receiver. However the dimensions of the WSN deployment are so large as to consider the effects of the near field. WSN with 200m diameter have Fresnel areas ranging to distances as far as 325Km. In the present work we present the theoretical background to analyze the SCB transmission of a cluster of radiating motes, and also the theoretical planning for a SCB long range communication in the Albufera de València natural reservation area, some preliminary simulations and measurements are also presented.

When designing and deploying multimedia systems, it is essential to accurately know about the necessary requirements and the Quality of Service (QoS) offered to the customers. This paper presents two open-source software tools that contribute to these key needs. The first tool is able to measure and register resources consumption metrics for any Windows program (i.e. process id), like the CPU, GPU and RAM usage. Unlike the Task Manager, which requires manual visual inspection for just a subset of these metrics, the developed tool runs on top of the Powershell to periodically measure these metrics, calculate statistics, and register them in log files. The second tool is able to measure QoS metrics from DASH streaming sessions by running on top of TShark, if a non-secure HTTP connection is used. For each DASH chunk, the tool registers: the round-trip time from request to download, the number of TCP segments and bytes, the effective bandwidth, the selected DASH representation, and the associated parameters in the MPD (e.g., resolution, bitrate). It also registers the MPD and the total amount of downloaded frames and bytes. The advantage of this second tool is that these metrics can be registered regardless of the player used, even from a device connected to the same network than the DASH player.

Soundscape standard (ISO 12913) is mainly oriented to describe the psychoacoustic annoyance (PA) due to the perceived sound in different environments. The evaluation of this annoyance is commonly based on the Zwicker and Fastl model that defines several components related to this subjective annoyance, such as loudness, sharpness, roughness, and fluctuation strength. But due to their complexity, these components are difficult to be calculated on small board computers (SBCs) in real time in order to enable a wireless acoustic sensor network for PA monitoring. In this article, we describe the necessary procedures to implement the complete psychoacoustic model by Zwicker and Fastl in a precise and efficient way, keeping the standards and criteria defined for each model, adding hints to improve its performance. We evaluate their execution time on different platforms and languages, including SBCs, in particular on Raspberry Pi family. Thus, this article shows a feasible implementation of this monitoring approach on low-cost platforms as well as a visualization tool.

Wireless Sensor Networks (WSNs) are based on tiny autonomous devices, usually named motes or nodes, and are used in many monitoring applications, such as environment, habitat, precision horticulture, etc. These motes are power-limited and most energy is wasted through the communication process when exchanging data packets. Because these motes are quite simple, with low power transmission and simple antennas, these networks usually run multihop routing protocols in order to forward packets between one mote to the sink, or the collector node, using the other motes as relaying motes. Basically, these routing protocols try to find out new routes within these wireless network, optimizing different criteria, such as number of hops, network life time, throughput, etc. However, if something is going wrong within the network (big interferences, problems in one node, unstable routes, etc.), due to the lack of information retrieved related to management purpose, it is difficult to fix it and many times the outcome is critical, leaving the network out of service. To avoid these issues, we show in this paper a web tool for path monitoring and analysis within the WSN in order to improve their performance and to plan ahead this kind of problems. In this paper, we show the different alternatives in the design of this tool and the different rules applied when trying to find out the inner problems in the network. In this kind of network, using the routing packets, we can get further information of the inner problems and infer them based on the statistics and rules applied.

Wireless Sensor Networks (WSNs) are composed of spatially distributed autonomous sensor devices, named motes. These devices are constituted by a microcontroller, sensors, and they have the ability to communicate in the ISM frequency band using the IEEE 802.15.4 standard. They have their own power supply, AA or AAA batteries, processing unit, sensors and wireless communications. Usually, the motes exchange packets using a multihop routing, and the maximum communication distance emitter-receiver is around 100m. At least one mote acts as a gateway, and the data gathered by the sensors of each mote have to be sent to this mote that is named sink or Base Station (BS). In a WSN the BS is within the area of the WSN in order to receive the information. The distance between the BS and the nearest mote must be at the most 100m to be able to receive the packets. If the BS distance is farther than this distance, the communication fails, because the motes have limited power. Some techniques are proposed to overcome this distance in order to achieve a long range communication to reach the BS, for instance to communicate to a low orbit satellite. The difficulties are related to synchronization errors in the clocks of the motes. We propose an alternative technique based in a collaborative beamforming scheme, named stochastic collaborative transmission or stochastic beamforming, in which we take advantage of the synchronization errors of the clocks to achieve a large gain in a limited number of trials.

The evaluation of QoS in mobile multipath environments is an interesting topic in modern communications. The use of Raspberry Pi as a low cost microcomputer board developed in the United Kingdom by the Raspberry Pi Foundation can be helpful in onsite deployments. The Pi3 model has integrated WiFi and Bluetooth connectivity and also 4 USB ports to expand the device with all kinds of peripheral. Under the premise of offering the power of a computer at low prices, this device is one of the most affordable ways to have a particularly efficient and dynamic hardware accessible at a low price to develop a Wireless Sensor Netwok (WSN). The use of the Raspberry Pi3 permits the development of a powerful and hight efficient mote in the development of a WSN, where data is highly processed in each mote and transmissions between motes and the sink or base station (BS) can be keep at minimum. We develop an experimental setup to analyse the quality of transmission between these devices in order to a further modelization of the channel. We program the Raspberry Pi3 to evaluate the quality of the transmission between two devices within a controlled multipath environment, particularly within an anechoic chamber.

Recent studies about the use of cloud platforms indicate that more than three-fourths of healthcare institutions apply these technologies in their daily lives. However, in most cases the cloud is based on “Infrastructure as a Service”, instead of developing new applications using the benefits that can bring us this type of technology. For these reasons, this paper shows a private cloud-based system to provide a new application based on thermographic video streaming. In this case, the cloud architecture is divided into three zones. The first zone is where the patient is under medical supervision through body temperature analysis using a global thermal camera. The second zone is the cloud itself, where the video is encoded, but taking into account that the received video must ensure a correct measurement of the temperature. The third zone is the access network, where people or processes receive and analyze these videos. Finally, a performance evaluation using several video codecs and bitrates is done in order to see which one is more accurate and fits better medical requirements. Finally, we conclude that the system works properly, indicating which video codecs are more suitable for each scenario.

We present the results from a measurement-based performance evaluation of wireless networks based on IEEE 802.11n and IEEE 802.11ac standards in an indoor environment, with the aim to analyze their performance under high definition streaming video applications. We focus our study on analyzing the highest performance of these standards using off-the-shelf equipment, both with synthetic TCP and UDP traffic to measure the saturation throughput as well as high definition video streams. The measurements have been conducted in the student labs of our university and show good performance for streaming purposes in high definition and also ultra-high definition from a subjective video quality point of view. IEEE 802.11ac performance is very sensitive to the channel conditions. According to our experiments, these results heavily depend on the implementation of the IEEE 802.11ac standard of the commercial access points.

The vast majority of localization approaches using phase transform (PHAT) consider that the sources of interest are wideband low-pass sources. While this may be the usual case for common audio signals such as speech, PHAT methods are affected negatively by modulation artifacts when the sources to be localized are passband signals. In these cases, steered response power PHAT localization becomes less robust. This letter analyzes the form of generalized cross-correlation functions with PHAT when passband acoustic signals are considered, proposing approaches for increasing the localization performance through the mitigation of these negative effects.

In this paper, we present an effective algorithm to reduce the number of wraps in a 2D phase signal provided as input. The technique is based on an accurate estimate of the fundamental frequency of a 2D complex signal with the phase given by the input, and the removal of a dependent additive term from the phase map. Unlike existing methods based on the discrete Fourier transform (DFT), the frequency is computed by using noise-robust estimates that are not restricted to integer values. Then, to deal with the problem of a non-integer shift in the frequency domain, an equivalent operation is carried out on the original phase signal. This consists of the subtraction of a tilted plane whose slope is computed from the frequency, followed by a re-wrapping operation. The technique has been exhaustively tested on fringe projection profilometry (FPP) and magnetic resonance imaging (MRI) signals. In addition, the performance of several frequency estimation methods has been compared. The proposed methodology is particularly effective on FPP signals, showing a higher performance than the state-of-the-art wrap reduction approaches. In this context, it contributes to canceling the carrier effect at the same time as it eliminates any potential slope that affects the entire signal. Its effectiveness on other carrier-free phase signals, e.g., MRI, is limited to the case that inherent slopes are present in the phase data.

The necessity of energy-efficient systems in order to protect our environment, cope with global warming, and facilitate sustainable development is paramount for the researching world because the survival of the planet is at stake. Thus, optimizing the energy efficiency of wireless communications not only reduces environmental impact, but also cuts overall network costs and helps make communication more practical and affordable in a pervasive setting. This paper is focused on a solution to enhance the energy efficiency in outdoor wireless local area networks using the standard IEEE 802.11b/g. So, from a previous study about the weather’s impact on the number of control frame errors and retransmissions, we propose a green cognitive algorithm that adapts wireless transmissions to the channel conditions caused by the weather. The goal is to reduce retransmissions and control errors in order to save energy and to enhance network performance. Our proposal is based on a mathematical analysis in which we see how the frame error rate is related to the power consumption according to the modulation scheme and data rate used by transmitters. Finally, several simulations show that the green cognitive algorithm presented in this paper involves significant energy savings for outdoors WLANs.

Some works on the researchers cooperation's literature provide the key lines for building research networks and propose new protocols and standards for business to business (B2B) data exchange, but none of them explains how researchers should contact and the procedure to select the most appropriate partner of a research enterprise, institute or university. In this paper, we propose a B2B architecture and protocol between research entities, that uses ebXML protocol. The contacts for cooperation are established based on some defined parameters and an information retrieval system. We explain the information retrieval system, the researcher selection procedure, the XML-based protocol and the workflow of our proposal. We also show the information that has to be exchanged to contact other researchers. Several simulations demonstrate that our proposal is a feasible architecture and may be used to promote the research cooperation. The main purpose of this paper is to propose an efficient procedure for searching project partners.

This paper presents a secure protocol for spontaneous wireless ad hoc networks which uses an hybrid symmetric/asymmetric scheme and the trust between users in order to exchange the initial data and to exchange the secret keys that will be used to encrypt the data. Trust is based on the first visual contact between users. Our proposal is a complete self-configured secure protocol that is able to create the network and share secure services without any infrastructure. The network allows sharing resources and offering new services among users in a secure environment. The protocol includes all functions needed to operate without any external support. We have designed and developed it in devices with limited resources. Network creation stages are detailed and the communication, protocol messages, and network management are explained. Our proposal has been implemented in order to test the protocol procedure and performance. Finally, we compare the protocol with other spontaneous ad hoc network protocols in order to highlight its features and we provide a security analysis of the system.

Improving the performance and the behavior of outdoor wireless local area networks (WLAN) is a very important objective for almost all researchers in the telematics field. In this paper, a clear relation between the weather and the performance of an outdoor WLAN is shown. This relation is extracted from a correlation analysis between several meteorological variables and the control and management frames of the data link layer of a WLAN at the Universitat Politècnica de València campus. In order to carry out this study, it was necessary to collect the required data during two months from an outdoor WLAN, as well as the meteorological variables from the weather station located at the same university campus. Then, we processed all this data and concluded that the temperature and the solar radiation affect the RTS frames, CTS frames, acknowledgments, and others.

New communication technologies must be provided in order to improve the energy distribution. As far as we know, there is not any network protocol designed exclusively for this purpose. In this paper, we present the design and simulation of a group-based protocol to improve the energy distribution in smart grids. We will show the group-based architecture and the protocol operation. Finally we will show some measurements taken from a test bench in order to test its performance.

One of the main issues in aquaculture is the high cost of the lost food when the fish are fed. Furthermore, this wasted food is deposited in the seabed and generates an environmental impact on the surrounding area. It causes failure to uphold aquaculture legislation. The authors propose a feeding control system based on groups of sensors that take the appropriate decisions when the fish are fed in marine fish farms. This system mixes data obtained from a group of sensors and performs an exhaustive control on the food offered to the fish, thus reducing costs. This will avoid wasting large quantities of food and an economic saving will be achieved. To develop the proposed system, the authors used the behaviour of the movements of the fishes and some parameters from the water. These movements are monitored strategically by a group ofunderwater transducers placed along the length, width and depth of the cage and, through data fusion, they provide the information needed in the system to continue feeding the fish or not.

Many wireless sensor network developers have adopted Bluetooth and Zigbee technologies for sensor communication. However, it has a big problem: the radio coverage is very short; so many sensors are needed to cover wide areas and sensors are expensive. On the other hand, these technologies lack a high bandwidth. IEEE 802.11a/b/g technologies have higher coverage areas and higher bandwidth, but their energy consumption is significantly higher. This paper discusses the energy consumption analytically and gives the considerations that should be taken into account in developing a wireless local area sensor network. Then, we propose a new protocol based on group collaboration to be implemented in these types of networks in order to decrease the energy consumption and save the energy wasted by the whole system. Finally, the protocol proposed is simulated and compared with other protocols available in the Ns2 simulator in order to prove that our solution is better.

A social network site (SNS) is a Web 2.0 software application used by many Internet users. These types of networks have their advantages and disadvantages. The main advantage is the degree of communication that they can bring among users. This can improve the work environment, the productivity and also the educational environment. The negative point of these networks is communication misuse. Sometimes, users can share private information that can lead them to trouble. In order to solve these problems two options could be used: (a) create a closed platform, which only feasible in working and educational environments, or (b) use a Web platform, where the data about users are anonymous. In this latter case, we present a platform to share pictures based on the Web 2.0, where user information is anonymous. In this platform, in addition to sharing photos, we can create groups, introduce comments, etc. The platform is based on PHP and MySQL. We present the evaluation of this SNS in a real environment during a month. The parameters measured show that the SNS is working fine. Nobody has measured the same parameters as the ones studied in this paper.

In contrast with the simple and elegant concept of global computing, also called ubiquitous computing - where daily life objects get now computing capacity and interact among them to provide a wide range of services to users - a major difficulty is arising with the software engineering process to develop applications for these devices. This article discusses the additional parameters that should be taken into account when designing and developing software for ubiquitous devices in ad-hoc networks. In these networks the mobility of the devices could increase the complexity of the requirements to be considered. As a case study, we present a Bluetooth application that has been developed to fulfill a variety of established requirements. The application allows the creation of different types of networks for non-expert users thanks to the context aware capacity of Bluetooth.