The Extended Berkeley Packet Filter (eBPF) is a sandboxed virtual machine that runs on operating systems with kernel privileges. Currently, eBPF programs are either translated from a subset of C, called Restricted C, or from bindings available for languages such as Rust or Python. This paper describes Honey Potion, a compiler that compiles Elixir to eBPF binaries. Translation is challenging, for it must not only preserve semantics, but also satisfy the eBPF verifier, which requires proofs of in-bounds memory accesses and termination. The translator relies heavily on this last constraint---ensured termination---to implement different optimizations: constant propagation, type specialization and partial evaluation. Honey Potion is publicly available, and has been used in the development of many eBPF applications, such as packet routers, process monitors and event loggers. To the best of our knowledge, Honey Potion is the first translator of a functional programming language to eBPF.

Network Function Virtualization promises better utilization of computational resources by dynamically scaling resources on demand. However, most network functions (NFs) are stateful and require state updates on a per-packet basis. During a scaling operation, cores need to synchronize access to a shared state to avoid race conditions and to guarantee that NFs process packets in arrival order. Unfortunately, the classic approach to control concurrent access to a shared state with locks does not scale to today’s throughput and latency requirements. Moreover, network traffic is highly skewed, leading to load imbalances in systems that use only sharding to partition the NF states. To address these challenges, we present Dyssect, a system that enables dynamic scaling of stateful NFs by disaggregating the states of network functions. By carefully coordinating actions between cores and a central controller, Dyssect migrates shards and flows between cores for load balancing or traffic prioritization without resorting to locks or reordering packets. Also, Dyssect’s state disaggregation allows the offloading of stateful network functions to programmable NICs and makes it easier for exploring hardware-software tradeoffs that better suit specific service chains and traffic loads. Our experimental evaluation shows that Dyssect reduces tail latency up to 32.04% and increases throughput up to 19.36% when compared to state-of-the-art competing solutions.

NFV and SDN enable flexibility and programmability at the data plane. In addition, offloading packet processing to a hardware saves processing resources to compute other workloads. However, fulfilling requirements such as high throughput and low latency with a flexible and programmable data plane is challenging. This paper introduces eBPFlow, a platform for seamlessly accelerating network computation. It builds upon eBPF. eBPFlow combines flexibility and programmability in software with high performance using an FPGA. We implemented our system on the NetFPGA SUME, performing tests on a physical testbed. We built a range of NFs. Our results show that the eBPFlow supports offloading of NFs with throughput at the line rate, latency between$20~\mu \text{s}$and$40~\mu \text{s}$, communication with host, and consumption of 22 W. Moreover, eBPFlow processes 12.05 Mpps more than the kernel. eBPFlow has a throughput of 2.59 Gbps higher than the hXDP, a system similar to eBPFlow.

Millions of videos are watched per minute on the Internet. Due to real-time performance demands, such as high-quality video streaming, network administrators face new challenges to control the network and cope with the expected quality of experience (QoE). Automatic control is a necessity to reduce the OPEX, because it could reduce the need for resource overprovisioning, as well as the number of human administrators. Dynamic rate in video streaming alleviates the resource usage, but it worsens the video quality when a network bottleneck occurs, lowering the QoE. This paper dynamically adjusts the IEEE 802.11 parameters to improve the network condition and hence maintain a higher QoE. While traditional networks are not aware of the application, in our proposal the controller learns the configuration of the access points (APs) (in terms of transmission power and channel number) that provide the best QoE, using double deep Q-Learning (DDQL). The proposal improves video QoE by 91 % in the best case, when compared to three baselines. It also balances the QoE among clients, improving the fairness up to 115% when compared to the baselines.

While most networks have long lifetimes, temporary network infrastructure is often useful for special events, pop-up retail, or disaster response. Aninstant IoTnetwork is one that is rapidly constructed, used for a few days, then dismantled. We consider the synthesis of instant IoT networks in urban settings. This synthesis problem must satisfy complex and competing constraints: sensor coverage, line-of-sight visibility, and network connectivity. The central challenge in our synthesis problem is quicklyscalingto large regions while producing cost-effective solutions. We explore two qualitatively different representations of the synthesis problems using satisfiability modulo convex optimization (SMC), and mixed-integer linear programming (MILP). The former is more expressive, for our problem, than the latter, but is less well-suited for solving optimization problems like ours. We show how to express our network synthesis in these frameworks. To scale to problem sizes beyond what these frameworks are capable of, we develop ahierarchical synthesistechnique that independently synthesizes networks in sub-regions of the deployment area, then combines these. We find that, while MILP outperforms SMC in some settings for smaller problem sizes, the fact that SMC's expressivity matches our problem ensures that it uniformly generates better quality solutions at larger problem sizes.

Network Functions (NFs) now process a significant fraction of Internet traffic. Software-based NF Virtualization (NFV) promised to enable rapid development of new NFs by vendors and leverage the power and economics of commodity computing infrastructure for NF deployment. To date, no cloud NFV systems achieve NF chaining, isolation, SLO-adherence, and scaling together with existing cloud computing infrastructure and abstractions, all while achieving generality, speed, and ease of deployment. These properties are taken for granted in other cloud contexts but unavailable for NF processing.

Network Function Virtualization promises better utilization of computational resources by dynamically scaling resources on demand. However, most network functions (NFs) are stateful and require state updates on a per-packet basis. During a scaling operation, cores need to synchronize access to a shared state to avoid race conditions and to guarantee that NFs process packets in arrival order. Unfortunately, the classic approach to control concurrent access to a shared state with locks does not scale to today’s throughput and latency requirements. Moreover, network traffic is highly skewed, leading to load imbalances in systems that use only sharding to partition the NF states. To address these challenges, we present Dyssect, a system that enables dynamic scaling of stateful NFs by disaggregating the states of network functions. By carefully coordinating actions between cores and a central controller, Dyssect migrates shards and flows between cores for load balancing or traffic prioritization without resorting to locks or reordering packets. Our experimental evaluation shows that Dyssect reduces tail latency up to 32% and increases throughput up to 19.36% when compared to state-of-the-art competing solutions.

Underwater wireless acoustic networks (UWANs) take advantage of acoustic communication to enable many distinct applications. The peculiar features of underwater acoustic channels, such as long propagation delay, high bit error rates, and severely limited bandwidth, make the use of medium access control (MAC) solutions designed for terrestrial radio networks inefficient in UWANs. This paper proposes UW-SEEDEX, a MAC protocol for UWANs that employs random time slot schedules, created from seeds, to avoid collisions. After exchanging seeds, nodes can know other’s entire schedules, allowing them to then better plan their transmissions. Simulations evaluate how each of UW-SEEDEX’s parameter affects its performance in metrics such as end-to-end delay, energy consumption, transmissions per data reception, and reception rate using different test scenarios. Simulations also show that UW-SEEDEX can perform better than other MAC solutions, delivering more messages than protocols such as UW-Aloha and Slotted FAMA and reduced, on average, up to three times the number of transmissions required for each message reception in networks with grid topologies. UW-SEEDEX presented reception rates close to 100 percent, low energy cost, and fewer transmissions per data reception. Our code is available athttps://gitlab.com/epmcj/ns-3-dev/-/tree/new-uan-mac-protocols.

Current Service Function Chaining (SFC) architectures are tailor-made for specific environments and platforms, often relying on SFC support on network devices or specialized frameworks. Thus, the service plane and the data plane are tightly coupled, which hinders innovation. For example, prototyping new SFC protocols usually requires re-implementing service functions (SFs) or modifying network devices. To address these issues, we propose Chaining-Box, a new SFC architecture based on a simple idea: implementing all the SFC functionality as a sequence of stages. This is done in a fully transparent manner without changing neither SFs nor network devices. Stages are implemented using BPF, a technology that allows user-defined programs to run inside the Linux kernel. The stages run as packets traverse the kernel stack and implement all SFC actions to provide the chaining. A proof-of-concept of Chaining-Box is implemented as a prototype, which demonstrates a decrease of 20%-40% in latency in comparison with similar proposals. The results also show equivalent performance when compared with OVS-based SFC, but allowing the bridges to be SFC-agnostic.

Public outdoor surveillance cameras often have limited metadata describing their properties. Frequently, a public camera’s precise position, orientation, focal length, and image center are unknown; these attributes are necessary to precisely pinpoint the location of events seen in the camera. In this article, we ask: what is the minimal information needed to accurately estimate these properties for public cameras? We show, using a judicious combination of projective geometry, neural networks, and crowd-sourced annotations from human workers, that it is possible to, for example, localize 95% of the cameras in our test data set to within 12 m using a single image taken from the camera. This performance is an order of magnitude better than PoseNet, a state-of-the-art neural network that needs significantly more information than our approach, and can only estimate position and orientation (and not other properties). Finally, we show that the camera’s inferred pose and properties can help design a number of virtual sensors , all of which have good accuracy.

Core allocation for application threads is a problem of reasonable complexity and computational cost inside Unix systems. Caladan scheduler is a solution aiming to reduce the cost of how threads and cores are allocated in microsecond scale. The Danian system optimizes through memoization the thread picking algorithm that picks the best thread for a given core. Such improvements have direct impact on applications distributed across networks on a data center. Thread picking operation cost dropped from O(n) to O(1), the CPU time reduced 7%, the tail latency reduced 3% on Caladan Synthetic experiment and 5% on the Netperf experiment.

QUIC protocol is considered an experimental environment and a straight forward evolution of the TCP protocol. Applications created on top of QUIC demonstrate being more efficient than the regular HTTPS stack. Computational offload to kernel space is used as performance optimization for modern applications and brings with it a series of architectural and algorithmic challenges. This work presents eQUIC Gateway, a kernel space module that filter packets based on real time information exchanged with a user space QUIC application. Throughout kernel offload technique for filtering packets, eQUIC Gateway increased the packets throughput in 30.9%, reduced 65 % of the average HTTPS request duration under attack and reduced by 26.4% the CPU time consumed for filtering packets. The source code of eQUIC Gateway is public available on Github.

Network Functions (NFs) perform on-path processing of network traffic. ISPs are deploying NF Virtualization (NFV) with software NFs run on commodity servers. ISPs aim to ensure that NF chains, directed acyclic graphs of NFs, do not violate Service Level Objectives (SLOs) promised by the ISP to its customers. To meet SLOs, NFV systems sometimes leverage on-path hardware (such as programmable switches and smart NICs) to accelerate NF execution.

Advances in optimization and constraint satisfaction techniques, together with the availability of elastic computing resources, have spurred interest in large-scale network verification and synthesis. Motivated by this, we consider the top-down synthesis of ad-hoc IoT networks for disaster response and search and rescue operations. This synthesis problem must satisfy complex and competing constraints: sensor coverage, line-of-sight visibility, and network connectivity. The central challenge in our synthesis problem is quickly scaling to large regions while producing cost-effective solutions. We explore a representation of the synthesis problems using a novel constraint satisfaction paradigm, satisfiability modulo convex optimization (SMC). We choose SMC because it matches the expressivity needs for our network synthesis. To scale to large problem sizes, we develop a hierarchical synthesis technique that independently synthesizes networks in sub-regions of the deployment area, then combines these. Our experiments show that SMC consistently generates better quality solutions than a baseline synthesis approach based on Mixed Integer Linear Programming (MILP).

Pursuit-Evasion Game (PEG) consists of a team of pursuers trying to capture one or more evaders. PEG is important due to its application in surveillance, search and rescue, disaster robotics, boundary defense and so on. In general, PEG requires exponential time to compute the minimum number of pursuers to capture an evader. To mitigate this, we have designed a parallel optimal algorithm to minimize the capture time in PEG. Given a discrete topology, this algorithm also outputs the minimum number of pursuers to capture an evader. A classic example of PEG is the popular arcade game, Pac-Man. Although Pac-Man topology has almost 300 nodes, our algorithm can handle this. We show that Pac-Man is overkill, i.e., given the Pac-Man game topology, Pac-Man game contains more pursuers/ghosts (four) than it is necessary (two) to capture evader/Pac-man. We evaluate the proposed algorithm on many different topologies.

In this paper, we study the localization problem under non-Gaussian noise. In particular, we consider systems that can be represented by a state transition and a measurement component. The state transition indicates how the system evolves given a control variable. The measurement component compares, for a given state, the received and predicted measurements. Here we consider a radio based range sensor which is the primary source of non-Gaussian noise in the system. We solve the problem using a MHE (Maximum Horizon Estimator) with a correntropy similarity metric. Given a time window, the MHE seeks the best set of states that explains the system for the received measurements. Moreover, the main advantage of a MHE is that it allows the re-estimation of past states. Additionally, the correntropy is a similarity metric that, given the amount of error in the estimation, behaves as L2, L1 or L0 norms and has been successfully used in many applications under non-Gaussian noise. We evaluate our proposed method using both simulated and real data. The results show that correntropy is able to work well in comparison with other methods in presence of impulsive noise.

The use of two radios per node increases the energy efficiency of wireless sensor networks. Given that data collection is one of the most important functions in wireless sensor networks, this paper presents and compares two new data collection protocols for wireless sensor networks with two radios, DCTP-A and DCTP-I. DCTP-A builds the collection tree alternating the radio band each node while DCTP-I builds two independent collection trees. The protocols were implemented in TinyOS and evaluated experimentally in a testbed in the physical world using the 900MHz and 2.4GHz radio bands, compared to the state of the art (CTP and CTP-Multi) and to each other, considering the metrics delivery rate, latency, throughput in a saturated network scenario, total number of messages and the cost of maintaining routes. The results show the gain of the protocols for wireless sensor networks with two radios. DCTP-A achieved almost 100% of delivery rate, while DCTP-I achieved up to 90% delivery rate with less number of beacons messages.

Monitoring underwater environments is still a hard and costly task. Indeed, electromagnetic and optical waves suffer high attenuation, being absorbed in a few meters and even acoustic communication presents low throughput and high bit error rate. Most of the existing approaches to enhance underwater communication performance relies on developing acoustic modems, multiple access to the communication channel and, data routing. In this paper, we present COPPER: a COoperative Protocol for PERvasive Underwater Acoustic Networks. COPPER synchronously/asynchronously works on top of TDMA method combined with an ARQ scheme based on selective repeat technique. It uses idle sensor nodes as relay nodes, enhancing communication space diversity. Our simulations show that, when compared to a non-cooperative protocol, COPPER enhances overall network performance metrics. For instance, it reduces packet error rate by 28.32% and increases goodput by 16.87% while spending less than 1% more energy.

Communications using wireless networks nowadays receives an increasing amount of users and devices. This implies in an increasing need for improvements in wireless networks. IEEE 802.11ac standard enables channels wider than 20 MHz bandwidth. But currently, access points allocate channel bandwidth statically, independent of the clients' configurations. This leads to poor network performance. In this work, we design a system that enables clients to choose the link's bandwidth they connect to the APs. We validate the system through realistic experiments. We show the trade-off between latency and throughput. Our results show that our system improves the frequency spectrum usage, and provide better throughput/latency for the user.

The link aggregation technique not only provides robustness to computer networks, but can also be a solution to the link saturation problem. This technique combines several physical interfaces to create a virtual link, adding up their existing bandwidth. In addition to increasing the throughput for data transmission, such a technique provides a quick and transparent recovery if a particular link becomes unavailable. Considering that the use of Software-Defined Networking (SDN) in business environments increases every day, this research presents a way to create link aggregations in such environments. This brings a better availability of services, among other benefits. To do so, an architecture that allows automatic, scalable and self-adaptive link aggregations was defined and implemented. As a way of evaluating such implementation, three algorithms were created using different premises: Hash Table, Traffic Analysis and Virtual Round-Robin. All implementations were tested in virtual and real environments. In both, the Open vSwitch open platform was used for packet switching and the Ryu controller was chosen to control the switches.

Many applications in Wireless Sensor Networks (WSNs) require collecting massive data in a coordinated approach. To that end, a many-to-one (convergecast) communication pattern is used in tree-based WSNs. However, traffic near the sink node usually becomes the network bottleneck. In this work, we propose an extension to the 802.15.4 standard for enabling wider bandwidth channels. Then, we measure the speed of data collection in a tree-based WSN, with radios operating in these wider bandwidth channels. Finally, we propose and implement Funneling Wider Bandwidth (FWB), an algorithm that minimizes schedule length in networks. We prove that the algorithm is optimal in regard to the number of time slots. In our simulations and experiments, we show that FWB achieves a higher average throughput and a smaller number of time slots. This new approach could be adapted for other relevant emerging standards, such as WirelessHART, ISA 100.11a and IEEE 802.15.4e TSCH.

The number of smart devices in home networks is rapidly increasing, making it more complex to manage their faults. In addition, the lack of customer knowledge and tools to automatically diagnose and fix faults aggravate the problem. In this paper, we propose HomeNetRescue, a Software-Defined Network (SDN) service for autonomous management of wireless and wired home networks focused on fault and configuration management. We evaluate HomeNetRescue in a real world prototype, considering throughput, delay, and jitter. Our results show that HomeNetRescue can increase the throughput of the network by up to 131%, reducing wireless transmission delay and jitter by 46% and 24%, respectively.

It is estimated that Internet traffic will triple in five years, which will increase server response time. One way to reduce such time is to balance the load on replicated servers using SDN switches. However, existing SDN switches have performance limitations that influence the performance of the load balacing. This work compares five load balancing policies: round robin, random, txbytes (transmitted bytes), cpuq-load (CPU usage and number of open connections), and load/load-prev (load forecast with switch statistics). These policies consider limitations such as the cost to retrieve network statistics and to install new rules. The results show that the txbytes and cpuq-load policies outperformed the others. On the other hand, the load-prev policy proved to be promising when adjusted for traffic.

Underwater networks are used for monitoring water resources and underwater environments. Thus, it is important that the underwater sensor nodes cover the largest region possible, during the largest amount of time. This paper presents a method to perform node scheduling in underwater stratified networks. It aims to maintain the network active for longer, maintaining its connectivity. Voronoi Diagrams are used to decompose the space into regions around each node in order to determine which one should be scheduled to sleep. Simulation results show that the proposed method achieves the desired objectives, more than doubling the network lifetime while guaranteeing connectivity.

Measurement and tracking have crucial roles in Software-Defined Networks (SDNs). Unfortunately, most of procedures and techniques to perform measurements and monitoring tasks are implemented in software at network end-hosts. Despite the large use, a software based approach generates imprecision, high costs, and makes monitoring more difficult. In this paper, we extend OpenFlow switch to implement a measurement architecture for SDN. Our system performs measurements in a simple and scalable way without depending on end-hosts. It allows monitoring the performance at the granularity of flows. Moreover, our system also enables software-defined measurements can collect flow's statistics on the fly. We have prototyped our architecture on the NetFPGA platform and, as an initial case study, we have implemented a module to measure packet interarrival time. This module has been validated in a realistic testbed. Our results demonstrate that the proposed architecture presents a negligible difference when compared to measurements performed by software at end-hosts.

Power line communication systems face several challenges which degrade data communication quality. To overcome such issues, we propose CodePLC, a network coding Power Line Communication MAC protocol. We use a single relay node to intermediate communication, storing, and forwarding linear combinations of data packets. We evaluate CodePLC performance through simulations of a common topology for a PLC system under a wide range of scenarios. In sum, our results show that in a broadcast like transmission, the use of network coding enhances overall system performance. When compared to a traditional PLC system, we have observed an average of 115% goodput increase. Moreover, our protocol reduces in 112% the average of network occupancy buffers. Finally, CodePLC reduces mean latency by four times.

Many applications of sensor networks collect massive data in a coordinated approach, such as monitoring volcanoes, oceans and forests. We propose, therefore, two protocols at the network stack to enable multiple frequency communication. MultiFCTP extends the state-of-the-art CTP to route packets using multiples frequencies. FlushMF is a transport protocol that extends Flush by supporting multiple frequencies. FlushMF is a reliable transport protocol and high throughput for massive data. FlushMF provides end-to-end acknowledgment, reduces the transfer time, and adapts to network conditions that vary in time. A network system that uses multiple frequencies can be more robust to interference and achieve better throughput. Our study shows that FlushMF provides higher throughput than Flush.

Standard routing protocols for IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) are mainly designed for data collection applications and work by establishing a tree-based network topology, which enables packets to be sent upwards, from the leaves to the root, adapting to dynamics of low-power communication links. The routing tables in such unidirectional networks are very simple and small since each node just needs to maintain the address of its parent in the tree, providing the best-quality route at every moment. In this work, we propose Matrix, a platform-independent routing protocol that utilizes the existing tree structure of the network to enable reliable and efficient any-to-any data traffic. Matrix uses hierarchical IPv6 address assignment in order to optimize routing table size, while preserving bidirectional routing. Moreover, it uses a local broadcast mechanism to forward messages to the right subtree when persistent node or link failures occur. We implemented Matrix on TinyOS and evaluated its performance both analytically and through simulations on TOSSIM. Our results show that the proposed protocol is superior to available protocols for 6LoWPAN, when it comes to any-to-any data communication, in terms of reliability, message efficiency, and memory footprint.

Wireless Networks have become ubiquitous and dense to support the growing demand from mobile users. To improve the performance of these networks, new approaches are required, such as context and service aware control algorithms, which are not possible on today's closed proprietary WLAN controllers. In this work, we propose Ethanol, a software-defined networking architecture for 802.11 dense WLANs. This paper describes the benefits of programmable APs, and proposes Ethanol, an architecture for network-wide control of QoS, user mobility, AP virtualization, and security on 802.11 APs. The proposal is evaluated on a prototype using off-the-shelf APs over three use cases.

In this work, we propose eXtend Collection Tree Protocol (XCTP), a routing protocol that is an extension of the Collection Tree Protocol (CTP). CTP is the de-facto standard collection routing protocol for Wireless Sensor Network (WSN). CTP creates a routing tree to transfer data from one or more sensors to a root (sink) node. But, CTP does not create the reverse path between the root node and sensor nodes. This reverse path is important, for example, for feedback commands or acknowledgment packets. XCTP enables communication in both ways: root to node and node to root. XCTP accomplishes this task by exploring the CTP control plane packets. XCTP requires low storage states and very low additional overhead in packets. With the reverse path, it is possible to implement reliable transport layer protocols for Wireless Sensor Network (WSN). Thus, we designed Transport Automatic Piggyback Protocol (TAP2), a transport protocol with Automatic Repeat-reQuest (ARQ) error-control on top of XCTP. We implemented these protocols on TinyOS and evaluated on TOSSIM. We compared XCTP with CTP, Routing Protocol for low-power and lossy networks (RPL), and Ad hoc On Demand Distance Vector (AODV) protocols. We conducted scalability and stress tests, evaluating them with different loads and number of nodes. Our results shows that XCTP is more reliable than CTP, delivering 100% of the packets. XCTP sends fewer control packets than RPL. XCTP is faster to recovery from network failures and also stores fewer states than AODV, thus being efficient and agile.

Networked robots can provide a communication substrate by establishing a wireless network backbone. Here, we investigate the problem of autonomously deploying robots to establish a wireless communication backbone so that a team of clients get connected. First, our approach calculates the Steiner Tree, considering even if the environment has obstacles. Then, we design a communicating extended finite state machine that allows robots to deploy the backbone autonomously. We prove that our approach needs a bounded number of networked robots. We validate our algorithm with simulations and experiments with physical robots in an indoor environment. We also evaluate the established backbone with network metric (end to end TCP throughput).

Despite great effort from the research community, wireless networks still operate below full capacity. To increase the network throughput it is important to study algorithms that select communication requests that can decode their signals despite mutual interference. In this paper, we study the joint problem of data rate assignment and link scheduling in the physical interference model. The objective of the problem is to maximize the total number of bits transmitted in one time slot. By constructing an intermediate network representation through a disk graph, we prove that a constant approximation solution can be computed in polynomial time. Finally, we propose a parallel implementation of a polynomial-time approximation scheme and show through simulations that the one-slot throughput of a wireless network can be significantly improved by using variable data rates.

Future wireless Internet envisions that thousands of embedded devices will be integrated to the Internet. These devices, which can be micro-controllers with radio and sensor components, require communication protocols to establish a network. But, some embedded devices have scarce RAM resource. Our work shows that it is possible to implement an IPv6 stack over Low power Wireless Personal Area Networks (6LowPAN) for RAM constrained systems. We are able to implement the RPL routing protocol with less than 512 bytes of RAM. To handle the hard RAM constraint, we develop a circular queue and a swap content mechanism from RAM to Flash memory. We develop a platform and tested the implementation with realist experiments. Our research has the potential for a great impact since it enables interconnecting many lower cost devices to the Internet.

We present NanoRouter, a new router architecture implemented as a quantum-dot cellular automata (QCA). A router is a key component in the Internet core. It allows packets to be transferred in the Internet. QCA is a promising nanoscale technology where components have nano size, ultra-low power consumption and could have a clock rate on the terahertz range. In a bottom-up approach, we first describe the building blocks that compose NanoRouter such as crossbar, demux and parallel-to-serial converter and then describe the full architecture. We demonstrate the functionality, test and validate the proposed architecture and provided performance evaluations of NanoRouter. This new router architecture can increase the speed of the Internet core.

Wireless communication technology offers multi-rate transmission capability which allows to increase the network's throughput. Nevertheless, previous algorithmic results have focused on single-rate radios. In this paper, we study the problem of scheduling multi-rate wireless requests considering the physical interference model. The objective of the problem is to select a subset of communication requests such that the sum of their data rates is maximized and no collisions occur if they are all scheduled simultaneously. We show that, under certain constraints on the input, the problem can be approximated by a graph-based model. More specifically, if network nodes live in a two-dimensional Euclidean space, where the path loss exponent is strictly larger than two, and if data rates and sender-receiver distances can only differ by a contact factor between communication requests, the problem can be modeled as a disk graph. This means that, despite the global nature of the physical interference model, conflicts between simultaneous requests can be restricted to the local neighborhood of the transmitting nodes. We show how to build the corresponding disk graph instances and prove that a weighted maximum independent set in this graph-based model provides a constant-factor approximation to the multi-rate scheduling problem in the physical interference model. Moreover, we implement a polynomial-time approximation scheme algorithm to obtain solutions that are within an arbitrarily small factor of being optimal in the disk graph model.

The research of underwater sensor networks (UWSNs) is gaining attention due to its possible applications in many scenarios, such as ecosystem preservation, disaster prevention, oil and gas exploration and freshwater reservoirs management. The main elements of a UWSN are underwater sensor nodes (UWNs). In this paper we present HydroNode: a low cost, energy efficient, multipurpose underwater sensor node (UWN). Nowadays, to the best of our knowledge, there is no UWNs that is simultaneously low cost, low power, able to couple diverse types of sensors and educationally available. Thus, the objective of this paper is to fill this gap by describing the design of HydroNode, an underwater sensor node that fulfill all these requirements and can be used in various UWSNs applications. We used only commercial off-the-shelf components to build our underwater sensor node. Due to its multipurpose design, HydroNode can be used in different UWSNs, therefore aiding the research of UWSN system protocols, configurations and applications.

In a setting that lacks infrastructure e.g., urban search and rescue, a team of networked mobile robots can provide a communication substrate by acting as routers in a wireless mesh network. We study the problem of determining the minimum number of robots, and how to position them, so that all clients using the resulting robotic network are connected and all network links satisfy minimum rate requirements. The key challenge we address is that in an environment with obstacles the strength of a wireless link is a non-monotonic function of the distance between the link end-points. Our approach to the problem is based on virtual potential fields. Clients and environmental obstacles are modeled as virtual charged particles exerting virtual forces on the robots. We validate our algorithm with physical robots in an indoor environment and demonstrate that we are able to get feasible solutions.

Most sensor network research and software design has been guided by an architectural principle that permits multinode data fusion on small-form-factor, resource-poor nodes, or motes . While we were among the earliest promoters of this approach, through experience we found that this principle leads to fragile and unmanageable systems and explore an alternative. The Tenet architecture is motivated by the observation that future large-scale sensor network deployments will be tiered , consisting of motes in the lower tier and masters , relatively unconstrained 32-bit platform nodes, in the upper tier. Tenet constrains multinode fusion to the master tier while allowing motes to process locally-generated sensor data. This simplifies application development and allows mote-tier software to be reused. Applications running on masters task motes by composing task descriptions from a novel tasklet library. Our Tenet implementation also contains a robust and scalable networking subsystem for disseminating tasks and reliably delivering responses. We show that a Tenet pursuit-evasion application exhibits performance comparable to a mote-native implementation while being considerably more compact. We also present two real-world deployments of Tenet system: a structural vibration monitoring application at Vincent Thomas Bridge and an imaging-based habitat monitoring application at James Reserve, and show that tiered architecture scales network capacity and allows reliable delivery of high rate data. 1

We study the collective transport of robots (CTR) problem. A large number of commodity mobile robots are to be moved from one location to another by a single operator. Joysticking each one or carrying them physically is impractical. None of the robots are particularly sophisticated in their ability to plan or reason. Prior work on flocking and formation control has addressed the transport of a robot group that maintains its integrity by explicitly controlling coherence. We show how flocking emerges as a consequence of each robot contending for space near the human operator. A coherent flock can be made to follow a leader in this manner thereby solving the CTR problem. We also present the design of a hand-worn IMU-based gesture interface which allows the human operator to issue simple commands to the group. A preliminary experimental evaluation of the system shows robust CTR with different leader behaviors.

Most sensor network research and software design has been guided by an architectural principle that permits multi-node data fusion on small-form-factor, resource-poor nodes, or motes. We argue that this principle leads to fragile and unmanageable systems and explore an alternative. The Tenet architecture is motivated by the observation that future large-scale sensor network deployments will be tiered, consisting of motes in the lower tier and masters, relatively unconstrained 32-bit platform nodes, in the upper tier. Masters provide increased network capacity. Tenet constrains multi-node fusion to the master tier while allowing motes to process locally-generated sensor data. This simplifies application development and allows mote-tier software to be reused. Applications running on masters task motes by composing task descriptions from a novel tasklet library. Our Tenet implementation also contains a robust and scalable networking subsystem for disseminating tasks and reliably delivering responses. We show that a Tenet pursuit-evasion application exhibits performance comparable to a mote-native implementation while being considerably more compact.

Language Emulator, written in Java, is a toolkit to help undergraduate students to understand the concepts of Automata Theory. The software allows the manipulation of regular expressions, regular grammars, deterministic finite automata, nondeterministic finite automata with and without lambda transitions, and Moore and Mealy machines. Language Emulator introduces error-detecting and internationalization functionalities into automata tools. It has been accepted by 95% of students in a recent survey, indicating that it is a helpful toolkit in learning Automata Theory.

Wireless sensor networks are networks of compact microsensors with wireless communication capability. These small devices are relatively cheap with the potential to be disseminated in large quantities. Emerging applications of data gathering range from the environmental to the military. As autonomous devices they can provide pervasive distributed and collaborative network of computer nodes. Architectural challenges are posed for designers such as computational power, energy consumption, energy sources, communication channels and sensing capabilities. Embedded Systems provide the computational platform for hardware and software components to interact with the environment and other nodes. This survey presents the current state-of-the-art for wireless sensor nodes, investigating and analyzing these challenges. We discuss the characteristics and requirements for a sensor node mainly processing, communications, power and sensing components. In this survey we present a comprehensive comparative study of sensor nodes platforms, energy management techniques, off-the-shelf microcontrollers, battery types and radio devices.

Power consumption became a crucial problem in the development of mobile devices, especially those that are communication intensive. In these devices, it is imperative to reduce the power consumption devoted to maintaining a communication link during data transmission/reception. The application of dynamic power management methodologies has contributed to the reduction of power consumption in general purpose computer systems. However, to further reduce power consumption in communication intensive real-time embedded devices, we have to consider the state of the computation and external events in addition to power management policies. In this paper we propose a model of an Extended Power State Machine (EPSM), where we adapt a Power State Machine to include the state of an embedded program in the power state machine formulation. This EPSM model is used to adapt the Quality of Service (QoS) in communication intensive devices to ensure low power consumption. In such development, a middleware layer fits in the system's architecture, being responsible for intercepting the data communication and implementing the EPSM. Also, a software tool was developed, allowing the Middleware Code to be generated based on the State Machine. A case study demonstrates the application of the proposed model to a real situation.

A wireless sensor network is a special kind of ad-hoc network with distributed sensing and processing capability that can be used in a wide range of applications, such as environmental monitoring, industrial applications and precision agriculture. Despite their potential applications, such networks have particular features imposed by resource restrictions, such as low computational power, reduced bandwidth and specially limited power source. In case of a network with a high density of sensor nodes, some problems may arise such as the intersection of sensing area, redundant data, communication interference, and energy waste. A management application is necessary to make the most of network resources. On the other hand, a high-density network can introduce a fault-tolerant mechanism, increase precision, and provide multi-resolution data. The network density control depends on the application. In this paper, we propose a method to set up which nodes should be turned off or on. The management may take the sensor node out of service temporally. Our design uses a Voronoi diagram, which decomposes the space into regions around each node. That schema could be used in management architecture for a wireless sensor network.