Federated Learning (FL) reshapes the AI model training paradigm by enabling privacy-preserving collaborative learning, where models are trained across distributed clients without sharing raw data, but only model parameters or updates. This learning can be centralized or distributed. Centralized FL (CFL) may suffer from latency and lack of robustness due to the reliance on a coordinating server for model convergence. On the other hand, Decentralized Federated Learning (DFL) enables direct collaboration among participating devices without relying on a central server. Each device can independently connect to other devices and share model parameters. In such collaborative training paradigm, model convergence in the presence of various deployment topologies, AI model types, Non-IID data distribution, and training strategies demands systematic analysis to realize their practical deployment in critical applications such as intelligent transportation, smart factories, and real-time surveillance. Some works have attempted to conduct only partial analysis and completely neglected incorporating Non-IID data distribution, a critical factor in practical deployment of DFL in mentioned applications. This work conducts a systematic analysis on the convergence of DFL considering a wide range of AI models (e.g., classical, deep neural networks, and Large Language Models), network topologies (e.g., linear, ring, star, and mesh), training strategies (e.g., continuous and aggregate), and degree of Non-IID data distributions. The analysis includes both mathematical formulations and their implementation and evaluation using real-world data. The results confirm that the convergence rate of the models is inversely proportional to the degree of Non-IID data distribution. Moreover, judicial selection of network topologies and training strategies can aid in this convergence process for the practical edge deployment of DFL.

In-network computing (INC) offloads parts of the functionality of a distributed system to programmable switches. Once we move the computation into the network, failures may cause loss of essential information and disrupt the operation of these systems. To ensure high availability in the event of switch failures, the current state-of-the-art replicates state across multiple INCs. They achieve this by usingstate-machine replication, ensuring that INC replicas are consistent bycoordinatingthe replication between multiple INC nodes. In this paper, we demonstrate that decoupling the consistency guarantees from the replication reduces the overhead of INC fault tolerance. We presentRESIST, a system for building fault-tolerant INC using asynchronous replication and replay-based recovery. We propose new techniques for logging information and different replay techniques to restore INC systems according to consistency semantics. Furthermore, we applyRESISTtechniques to existing INC functionalities, including event synchronization for distributed simulations and aggregation for distributed training. Our prototype ofRESISTenables fault tolerance for INC applications on BMv2 and in a testbed with Tofino ASICs. Experiments show that the system provides fault tolerance with negligible overhead for non-failure scenarios and recovers from failures in less than 0.2 s.

Ensuring consistent performance becomes increasingly challenging with the growing complexity of applications in data centers. This is where load balancing emerges as a vital component. A load balancer distributes network or application traffic across various servers, resources, or pathways. In this article, we present P4WISE, a load balancer designed for software-defined networks. Operating on both the data and control planes, it employs reinforcement learning to distribute computational loads with granularity at inter and intra-server levels. Evaluation results demonstrate a remarkable 90% accuracy in predicting the optimal load balancing strategy of P4WISE in dynamic scenarios. Notably, unlike supervised or unsupervised methods, it eliminates the need for retraining when the environment undergoes minor or major changes. Instead, P4WISE autonomously adjusts and retrains itself based on observed states within the data center.

Mission-critical applications (MCA) like smart grid management, first-aid response, and tactical coordination in military search and rescue operations refer to applications that can pose a risk to human lives or cause extensive and catastrophic losses. The deployment and management of these applications need careful consideration to meet the stringent performance demand of resource-constrained environments. One way to achieve such performance and dependability demand is to adopt Message Oriented Middlewares (MOMs) (e.g., Apache Kafka, RabbitMQ) as they enable real-time data analytics and informed decision making. Despite their extensive usage in legacy business intelligent applications, little is known about their suitability for mission-critical applications. This paper fills that gap by first deploying and testing mission-critical applications on Apache Kafka and RabbitMQ. Then, we measure the performance, security, and reliability of the chosen MOMs to support MCA. The evaluation results confirm that Apache Kafka outperforms RabbitMQ, making it a potential candidate to deploy MCA. Specifically, Kafka requires 13x less bandwidth than RabbitMQ, which could be further reduced by 85% using effective parameter tuning. Our findings pave the way for MOMs to be adopted in MCAs to meet their stringent performance and dependability demands.

Infrastructure as Code (IaC) automates the creation, configuration, management, and monitoring of computing infrastructure through code. One of the key principles that IaC promises is repeatability and reproducibility. However, certain programming practices in IaC platforms, especially those that allow imperative configuration, such as Ansible, hinder reproducibility in IaC scripts. This study, first, identifies such programming practices that we refer to as reproducibility smells by conducting a comprehensive multi-vocal literature review and propose a first-ever validated catalog of reproducibility smells for IaC scripts. We implement a tool viz. Reduse to identify reproducibility smells in Ansible scripts. Furthermore, we conduct an empirical study to reveal the proliferation of reproducibility smells in open-source projects and explore correlation and fine-grained co-occurrence relationships among them. We observe that broken dependency chain smell occurs the most in approximately $71 \%$ tasks that we analyzed. Our analysis uncovers significant positive correlations between specific reproducibility smells, implying that repositories with one such smell tend to exhibit others. Moreover, the co-occurrence analysis reveals smell pairs that show a high tendency of co-occurrence at the task granularity. With the developed tool Reduse, DevOps engineers can identify and rectify reproducibility issues before becoming part of the production system. Software engineering researchers can use the smells catalog proposed first in this study and can utilize Reduse in empirical studies exploring various facets of reproducibility.

The performance benefits of data plane programmability have motivated many researchers to offload the computation of applications that previously operated only on servers to the network, creating the notion of in-network computing (INC). Because failures can occur in the data plane, fault tolerance mechanisms are essential for INC. However, INC operators and developers must manually set fault tolerance requirements using domain knowledge to change the source code. These manually set requirements may take time and lead to errors in case of misconfiguration. In this work, we present ARAUCARIA, a system that composes fault tolerance building blocks for INC based on high-level intents. The system allows the specification of requirements using an intent language, which allows the expression of consistency and availability requirements in a constrained natural language. A refinement process translates the intent and instruments the INC with essential building blocks and configurations. Our prototype of ARAUCARIA enables fault tolerance for INC applications on BMv2 and in a testbed with Tofino ASICs. Experiments show that the system provides fault tolerance with negligible overhead.

Industrial Internet of Things (IIoT) networks must meet strict reliability, latency, and low energy consumption requirements. However, traditional low-power wireless protocols are ineffective in finding a sweet spot for balancing these performance metrics. Recently, network flooding protocols based on Synchronous Transmissions (STX) have been proposed for better performance in reliability-critical IIoT, where simultaneous transmissions are possible without packet collisions. STX-based protocols can offer a competitive edge over routing-based protocols, particularly dependability. However, they notably suffer from the beating effect, a physical layer phenomenon that results in sinusoidal interference across a packet and, consequently, packet loss. Thus, we introduce STX-Vote, an error correction scheme that can handle errors caused by beating effects. Importantly, we utilize transmission redundancy already inherent within STX protocols so do not incur additional on-air overhead. Through simulation, we demonstrate STX-Vote can substantially increase reliability. We subsequently implement STX-Vote on nRF52840-DK devices and perform extensive experiments. The results confirm that STX-Vote improves reliability by 25-28% for BLE 5 PHYs and 8% for IEEE 802.15.4; thus, it can complement existing error correction schemes.

In this work, we characterize the potential information leakage from IoT platforms during their setup phase. Setup involves an IoT device, its ''app'', and a cloud-based service. We assume that the on-device firmware is inaccessible, e.g., read-protected. We focus on the combination of information that can be extracted from analyzing the app and the local communication between the app and the IoT device. An attacker can trivially obtain the app, analyze its operation, and potentially eavesdrop on the wireless communication occurring during the setup phase. We develop a semi-automated general methodology involving off-the-shelf tools to examine information disclosure during the setup phase. We tested our methodology on twenty commodity-grade IoT devices. The outcome reveals a wide range of device-dependent choices for encryption at various layers and the potential for exposure of, among other things, device-identifying information and local networking (WiFi) credentials. Our methodology contributes towards a means to assess and ''certify'' IoT devices.

The rise of 5G deployments has created the environment for many emerging technologies to flourish. Self-driving vehicles, Augmented and Virtual Reality, and Remote Surgery are examples of applications that leverage 5G networks’ support for extremely low latency, high bandwidth, and increased throughput. However, the complex architecture of 5G hinders innovation due to the lack of accessibility to testbeds or realistic simulators with adequate 5G functionalities. Also, configuring and managing simulators are complex and time consuming. Finally, the lack of adequate representative data hinders the data-driven designs in 5G campaigns. Thus, we calibrated a system-level open-source simulator, Simu5G, following 3GPP guidelines to enable faster innovation in the 5G domain. Furthermore, we developed an API for automatic simulator configuration without knowing the underlying architectural details. Thus, researchers can use the calibrated and automated simulation to test various 5G functionalities and generate realistic operational data for data-driven designs.

Video analytics are often performed as cloud services in edge settings, primarily to offload computation and also in situations where the results are not directly consumed at the video source. Sending high-quality video data from end devices can be expensive in terms of both bandwidth and power use. To build a streaming video analytics pipeline that makes efficient use of these resources, it is imperative to reduce the size of the video streams. Traditional video compression algorithms are unaware of the semantics of the video, and can be both inefficient and harmful to the analytics performance. In this paper, we introduce LtC, a collaborative framework between the video source and the analytics server that efficiently learns to reduce the video streams within an analytics pipeline. Specifically, LtC uses the full-size video analytics algorithm at the server as a teacher to train a lightweight student neural network, which is then deployed at the video source. The student network is trained to capture the semantic significance of different regions within a video, which is used to selectively preserve the crucial regions in high quality while aggressively compressing the remaining regions. Furthermore, LtC incorporates a novel temporal filtering algorithm based on feature differencing to omit transmitting frames that do not contribute new information. Overall, LtC reduces bandwidth usage by 28-35% and attains a response delay that is up to 45% shorter than current state-of-the-art methods, while maintaining comparable analytics performance.

Programmable accelerators enable the execution of applications intended for running in usual servers. However, inappropriately running applications on these devices can lead to load imbalance and performance degradation. An alternative to tackle this problem is load balancing, but existing in-network load balancers typically have no visibility of accelerators and often hard code policies in the switch source code. In this article, we presentP4Hauler, an accelerator-aware in-network load balancer. In particular, our design discusses how to enforce load-balancing decisions in a programmable switch in a resource-aware manner, allowing different policies to handle traffic according to applications' needs. We use monitoring and compression techniques to store application resources in a programmable switch for resource-aware decisions. In addition, we propose building blocks that operators can dynamically choose to realize different load balancing policies on-the-fly. We implemented and evaluated a prototype ofP4Hauleron a testbed to show its efficiency and deployment feasibility. Our results indicate thatP4Haulercan support 27% more load and decrease the flow completion time by around 13% using only a single accelerator. Also, extensive simulations confirm the performance gain ofP4Haulerat scale compared to the state-of-the-art.

The massive growth in popularity of household IoT devices has brought new capabilities to our lives while also bringing new challenges to network providers. In particular, large numbers of devices have been used to cause disruptions to critical Internet services. Understanding which devices are connected to a network empowers administrators to mitigate threats with target-specific interventions. This information is obtained by analyzing traffic through a process known as device fingerprinting. Current device fingerprinting solutions face major scalability issues in high-speed and high-volume networks, either relying on middleboxes to perform their tasks or targeting a single household. This paper introduces a novel in-network fingerprinting system, PoirIoT, capable of real-time, accurate, and scalable IoT device fingerprinting. Specifically, PoirIoT takes advantage of recent programmable switches and use standard packet metadata, length, and direction information to gain high-throughput and per-packet fingerprinting granularity. We show the effectiveness (100% device detection accuracy) of our solution using a publicly available dataset on a testbed consisting of Intel Tofino switches. Moreover, PoirIoT adds no additional latency to the regular traffic flow and utilizes minimal switch resources (e.g., memory).

Stream processing applications have been widely adopted due to real-time data analytics demands, e.g., fraud detection, video analytics, IoT applications. Unfortunately, prototyping and testing these applications is still a cumbersome process for developers that usually requires an expensive testbed and deep multi-disciplinary expertise, including in areas such as networking, distributed systems, and data engineering. As a result, it takes a long time to deploy stream processing applications into production and yet users face several correctness and performance issues. In this paper, we present stream2gym, a tool for the fast prototyping of large-scale distributed stream processing applications. stream2gym builds on Mininet, a widely adopted network emulation platform, and provides a high-level interface to enable developers to easily test their applications under various operating conditions. We demonstrate the benefits of stream2gym by prototyping and testing several applications as well as reproducing key findings from prior research work in video analytics and network traffic monitoring. Moreover, we show stream2gym presents accurate results compared to a hardware testbed while consuming a small amount of resources (enough to be supported in a single commodity laptop even when emulating a dozen of processing nodes).

5G networks enable emerging latency and bandwidth critical applications like industrial IoT, AR/VR, or autonomous vehicles, in addition to supporting traditional voice and data communications. In 5G infrastructure, Radio Access Networks (RANs) consist of radio base stations that communicate over wireless radio links. The communication, however, is prone to environmental changes like the weather and can suffer from radio link failure and interrupt ongoing services. The impact is severe in the above-mentioned applications. One way to mitigate such service interruption is to proactively predict failures and reconfigure the resource allocation accordingly. Existing works like the supervised ensemble learning-based model do not consider the spatial-temporal correlation between radio communication and weather changes. This paper proposes a communication link failure prediction scheme based on the LSTM-autoencoder that considers the spatial-temporal correlation between radio communication and weather forecast. We implement and evaluate the proposed scheme over a huge volume of real radio and weather data. The results confirm that the proposed scheme significantly outperforms the existing solutions.

Low-power wireless IoT networks have traditionally operated over a single physical layer (PHY) - many based on the IEEE 802.15.4 standard. However, recent low-power wireless chipsets offer both the IEEE 802.15.4 and all four PHYs of the Bluetooth 5 (BT 5) standard. This introduces the intriguing possibility that IoT solutions might not necessarily be bound by the limits of a single PHY, and could actively or proactively adapt their PHY depending on RF or networking conditions (e.g., to offer a higher throughput or a longer radio range). Several recent studies have explored such use-cases. However, these studies lack comprehensive evaluation over various metrics (such as reliability, latency, and energy) with regards to scalability and the Radio Frequency (RF) environment. In this work we evaluate the performance of IEEE 802.15.4 and the four BT 5 2.4GHz PHY options for the recently completed IETF 6TiSCH low-power wireless standard. To the best of our knowledge, this is the first work to directly compare these PHYs in identical settings. Specifically, we use a recently released 6TiSCH simulator, TSCH-Sim, to compare these PHY options in networks of up to 250 nodes over different RF environments (home, industrial, and outdoor), and highlight from these results how different PHY options might be better suited to particular application use-cases.

IoT devices such as wearables, voice assistants and home appliances are becoming an integral part of our lives. However, these devices still represent a security and privacy risk with large-scale coordinated attacks often populating the news. The ability to tell which IoT devices are where in a network (i.e., to fingerprint them) can help administrators to mitigate such attacks at the earliest stages. While fingerprinting solutions exist, they often work offline, depend on sampled data or rely on payload information to work. In this paper, we propose PoirIoT, a high-speed in-network system for fingerprinting IoT devices. PoirIoT is based only on packet metadata (e.g., length and direction) and can detect a device as soon as it exchanges its first packets. We implement a prototype of PoirIoT on a Tofino-based programmable switch and show it can detect all possible IoT devices on a publicly available dataset. Moreover, PoirIoT runs at line rate and incurs minimal resource overhead on the programmable switch ASIC.

Stream processing applications are becoming increasingly important in areas such as IoT, video analytics and social media. As a result, developers and operators must meet stringent time-to-market and scale requirements before bringing them to production. Unfortunately, testing a networked stream processing system is currently a cumbersome process that usually requires an expensive testbed and deep expertise on both networking and distributed systems. In this poster, we present Raptor, a tool for the fast prototyping of large-scale networked stream processing applications. Raptor builds on Mininet and Apache Kafka, two widely adopted platforms, to enable stakeholders to easily test their solutions under various operational conditions. Through a reasonably large setup (20 nodes) running on a single server, we show how unbalanced Kafka's leader selection algorithm can be and its implications on the overall system's throughput. We envision this work can help paving the way for more reproducible research in the stream processing domain, currently a first-class network application.

Computer vision is a crucial component in many modern applications (e.g., medical image analysis, environmental monitoring and self-driving cars). However, their stringent computational, latency and bandwidth requirements still pose a huge challenge to system architects, which must seek for alternatives to both the limited resources (e.g., low-end CPU) on client devices and the hurdles of moving data from clients to cloud/edge servers for analysis. In this work, we advocate for the usage of emerging programmable network devices to speed up ML-based computer vision tasks, particularly image classification, on resource constrained environments. To take the first step towards this new paradigm, we propose NetPixel, a framework that enables P4-programmable switches to classify images in realtime, accurately and at scale. We implemented a prototype of NetPixel in a software switch to show its feasibility and conducted a preliminary evaluation on widely adopted datasets. Our results show that NetPixel can classify images with an accuracy within 8% that of a server-based implementation even for shallow classifiers and low-resolution images.

The flexible and programmable architectural model offered by Software-Defined Networking (SDN) has re-imagined modern networks. Supported by powerful hardware and high-speed communications between devices and the controller, SDN provides a means to virtualize control functionality and enable rapid network reconfiguration in response to dynamic application requirements. However, recent efforts to apply SDN’s centralized control model to the Internet of Things (IoT) have identified significant challenges due to the constraints faced by embedded low-power devices and networks that reside at the IoT edge. In particular, reliance on external SDN controllers on the backbone network introduces a performance bottleneck (e.g., latency). To this end, we advocate a case for supporting Software-Defined IoT networks through the introduction of lightweight SDN controllers directly on the embedded hardware. We firstly explore the performance of two popular SDN implementations for IoT mesh networks, $\mu$ SDN and SDN-WISE, showing the former demonstrates considerable gains over the latter. We consequently employ $\mu$ SDN to conduct a study of embedded vs. external SDN controller performance. We highlight how the advantage of an embedded controller is reduced as the network scales, and quantify a point at which an external controller should be used for larger networks.

Recently Unmanned Aerial Vehicles (UAVs) or Drones have gained enormous attention in applications like military, agriculture, industry, etc. One approach of controlling the operation of a drone is using hand gestures, which enables designing a low-cost system. However, the accuracy of such a system highly depends on the gesture recognition models. We can use a neural network-based gesture recognition model, which is a widely accepted image recognition scheme. In this work, we first design three deep neural network-based gesture recognition models: simple Convolutional Neural Networks (CNN), VGG-16, and ResNet-50 to uncover the best model for drone control. We evaluate the proposed models over our generated hand-gesture images in terms of their accuracy, precision, and complexity. The analysis reveals that each of the three models has its advantages and disadvantages while balancing between accuracy and complexity. For example, Simple CNN offers 92% accuracy on the testing set validation with the lowest validation loss compared to VGG-16 and ResNet-50. Thus, users can choose one of the proposed models to match their drone application.

In software-defined WANs (SD-WAN), link failure can lead to congestion and packet loss, hence degrading application performance. State-of-the-art traffic engineering approaches can speed up failure recovery by proactively installing backup tunnels and redirecting affected traffic immediately in the data plane, which reduces the burden on the network controller. However, these approaches either lead to bandwidth waste because of reserved link capacity or impose restrictions on network topologies, e.g., the existence of link-disjoint routes or large switch memory resources. In this paper, we propose SafeGuard, a software-defined proactive recovery system that improves bandwidth allocation and switch-memory usage while working on any connected network. We formulate the failure recovery problem as a multi-objective MILP optimization problem for all possible single link failures, the most common case in current WANs as temporally-coinciding failures are rare. We then develop a heuristic to efficiently compute backup routes as the problem is NP-Hard. We implemented a prototype of SafeGuard using the Ryu SDN controller and extensively evaluate it in Mininet over two real topologies, Google B4 and ATT. Our results show that SafeGuard can reduce the number of congested links by up to 50% compared to the state-of-the-art failure recovery scheme.

This poster presents P4-EncKV, an in-network proxy for accelerating encrypted data stores using recent programmable switches. P4-EncKV can perform operations over encrypted data while reducing query latency and required bandwidth. As proof-of-concept, we implement a prototype of P4-EncKV using BMv2 software switch, and show it can speedup encrypted queries by 20-25% using basic caching operations. Our optimized cache design also reduces memory consumption by 18% compared to the state-of-the-art in-network caching approach, thanks to a novel hash-based indexing scheme.

Communication link failure is common in any network. In Software Defined Network (SDN), protection-based recovery scheme reduces the failure recovery delay by installing alternative routes at the data plane switches. We can deploy Fast Failure Group (FFG) of OpenFlow protocol if a switch has an alternative path towards the destination; otherwise, the switch can use crankback approach to send the affected traffic towards the traversed route to find an alternative path. These existing recovery schemes force every packet to traverse a chain of matching tables even in the absence of a link failure, which impacts packet processing time and end-to-end delay. In this paper, we propose a packet rerouting architecture, called SD-FAST, that invokes recovery scheme only after facing failure and reduces both the packet processing and crankback backtracking time. We evaluate SD-FAST in Mininet, considering real and simulated traffic on real network topologies. The evaluation results confirm that SD-FAST can reduce around 73% crankback backtracking time and 64% delay compared to its counterparts.

Software-Defined Networking (SDN) can help simplify the management of today's complex networks and data centers. SDN provides a comprehensive view of the network, offering flexibility and easing automation. In SDN, traffic management functionality requires a high-performance and responsive controller. In this paper, we conduct an experimental evaluation of two open-source distributed OpenFlow controllers, namely ONOS and OpenDaylight. Specifically, we construct a testbed and use a standard benchmarking tool called Cbench to evaluate their performance. We benchmark the throughput, latency, and thread scalability of these two controllers in both physical and virtualized (OpenStack) environments. The experimental results show that ONOS provides higher throughput and lower latency than OpenDaylight, which suffers from performance problems on larger network models. Additional experiments demonstrate the effects of thread placement on the performance of these two controllers.

Smart camera networks (SCNs) are increasingly used in applications such as homeland security, border control and traffic monitoring. The cameras are often wireless with an organically growing structure, to reduce the overhead of deployment. We frame a new and important problem in SCNs: how to transmit videos from multiple cameras with overlapping coverage given the limited available wireless bandwidth to maximize the quality of the received videos. We call this problem Coordinated Transport of Correlated Videos (CTCV). CTCV is a more general version of 3D video transport: in that problem, highly correlated videos from two cameras (that provide the 3D perspective) are jointly encoded exploiting their pre-defined and known overlap. In contrast, in CTCV there is an arbitrary number of cameras whose overlap is not known apriori and that require transmission as multiple video streams. To effectively support CTCV, we propose a video delivery protocol that consists of two primary components: (1) Consolidation of correlated videos from multiple cameras which removes spatially redundant fields-of-view; and (2) Network and coverage aware bandwidth allocation to optimize coverage quality cooperatively among the different video streams to match the available bandwidth. We formulate the problem of optimal bandwidth allocation for maximizing coverage. We propose and investigate different heuristic policies for bandwidth allocation. We evaluate CTCV using data from a small camera testbed as well as topologies from realistic deployments. Experiments show that CTCV achieves around 10 dB gains in video quality in the scenarios we consider.

Wireless Sensor Networks (WSNs) have a wide range of applications including smart home and environmental monitoring systems. Sensors in such systems gather and propagate critical information to sinks (data collectors). Reliability is a crucial issue in these networks but the wireless medium is subject to interference and noise that can disrupt transmissions. Thus we design a reliable routing topology construction framework for WSNs and evaluate the proposed reliable topologies' performance. We observe that there are tradeoffs of reliability, energy consumption and path properties that applications can take advantage of.

In this paper, the problem of localized routing in mobile ad hoc networks is considered. In localized routing algorithms, messages are forwarded by each node based on its own geographic location, the location of its neighbors and the destination. In this work, a set of new geographic or position-based routing algorithms called the Randomized Greedy-AB-Greedy (RGABG) for routing in mobile ad hoc networks is proposed. The results of experiments on unit disk graphs and their associated Yao and Gabriel graphs show that the delivery rate of the studied routing algorithms is better than that of the deterministic greedy and compass algorithms and similar to that of the randomized AB routing algorithms. While the deterministic algorithms have the best path dilation, the path dilation of RGABG algorithms is close to the best result and better than the AB algorithms.

Abstract We consider the problem of localized energy aware routing in mobile ad hoc networks. In localized routing algorithms, each node forwards a message based on the position of itself, its neighbors and the destination. The objective of energy aware routing algorithms is to minimize the total power for routing a message from source to destination or to maximize the total number of routing tasks that a node can perform before its battery power depletes. In this paper we propose new localized energy aware routing algorithms called OLEAR. The algorithms have very high packet delivery rate with low packet forwarding and battery power consumption. In addition, they ensure good energy distribution among the nodes. Finally, packets reach the destination using smaller number of hops. All these properties make our algorithm suitable for routing in any energy constrained environment. We compare the performance of our algorithms with other existing energy and non‐energy aware localized algorithms. Simulation experiments show that our algorithms present comparable energy consumption and distribution to other energy aware algorithms and better packet delivery rate. Copyright © 2006 John Wiley & Sons, Ltd.

We consider the problem of localized energy aware routing in mobile ad hoc networks. In localized routing algorithms, each node forwards a message based on the position of itself, its neighbors and the destination. The objective of energy aware routing algorithms is to minimize the total power for routing a message from source to destination or to maximize the total number of routing tasks that a node can perform before its battery power depletes. In this paper we propose a new localized energy aware routing algorithm called OLEAR. The algorithm shows a high packet delivery rate with low packet forwarding and battery power consumption. In addition, it ensures a good energy distribution among the nodes and packets reach their destinations using smaller number of hops. All these properties make our algorithm suitable for routing in any energy constrained environment. We compare the performance of OLEAR with existing energy and non energy aware localized algorithms. Simulation experiments show that OLEAR presents comparable energy consumption and distribution to other energy aware algorithms and better packet delivery rates.

We consider the problem of energy aware localized routing in ad hoc networks. In localized routing algorithms, each node forwards a message based on the position information about itself, its neighbors and the destination. The objective of energy aware routing algorithms is to minimize the total power for routing a message from source to destination or to maximize the total number of routing tasks that a node can perform before its battery power depletes. In this paper we extend our previous work on randomized localized routing algorithms that achieve high packet delivery rates and show that they have good overall power consumption. We present two different variants of energy aware randomized routing, namely greedy and compass, and we study their performance using different cost metrics (e.g., forwarding power, remaining node energy, or a combination of both). We study their performance experimentally on different topologies and compare it with other existing algorithms. Our simulation results show that energy aware randomized algorithms achieve superior packet delivery rates and moderate energy consumption.