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An adaptive energy-efficient MAC protocol for wireless sensor networks
Abstract and Figures
In this paper we describe T-MAC, a contention-based Medium Access Control protocol for wireless sensor networks. Applications for these networks have some characteristics (low message rate, insensitivity to latency) that can be exploited to reduce energy consumption by introducing an active/sleep duty cycle. To handle load variations in time and location T-MAC introduces an adaptive duty cycle in a novel way: by dynamically ending the active part of it. This reduces the amount of energy wasted on idle listening, in which nodes wait for potentially incoming messages, while still maintaining a reasonable throughput. We discuss the design of T-MAC, and provide a head-to-head comparison with classic CSMA (no duty cycle) and S-MAC (fixed duty cycle) through extensive simulations. Under homogeneous load, T-MAC and S-MAC achieve similar reductions in energy consumption (up to 98%) compared to CSMA. In a sample scenario with variable load, however, T-MAC outperforms S-MAC by a factor of 5. Preliminary energy-consumption measurements provide insight into the internal workings of the T-MAC protocol.
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... Portanto, não é necessário manter os nós ativos no decorrer do tempo [Polastre et al., 2004]. Em relação ao consumo de energia, há possibilidade de variar a operação cíclica dos nós da rede entre dois períodos, ativos e inativos [Dam & Langendoen, 2003;Ye et al., 2004] [Rohde, 2009] ...
... Similar ao S-MAC, o protocolo T-MAC (Timeout -MAC )[Dam & Langendoen, 2003], voltado para coleta periódica e contínua, visa reduzir o tempo de escuta ociosa, utilizando ciclos dinâmicos de atividade e dormência, para diminuir o consumo de energia do nó. O controle do tempo ativo é feito por um temporizador, que ao seu término desliga o rádio, este temporizador não utiliza um intervalo fixo, o nó escuta a rede, transmite e recebe dados durante o seu tempo ativo. ...
Uma das principais aplicações existentes em Redes de Sensores Sem Fio (RSSFs) é o monitoramento ambiental, mais particularmente no acompanhamento da fauna existente em locais diversos como florestas, campos, etc. Nesses ambientes de difícil acesso, a manutenção dos sensores fica prejudicada e a restrição de energia torna-se um fator primordial para a vida útil da rede. Como na maioria desses casos os dados não são necessários em tempo real, neste trabalho é proposto e avaliado um novo algoritmo cross-layer, denominado PWAVE, voltado para RSSFs com coleta periódica, onde as transmissões dos dados são feitas através de intervalos predeterminados. Enquanto essas transmissões não são executadas, o sensor permanece coletando as informações do ambiente e seus rádios desligam-se, a fim de reduzir o consumo de energia. Diversos outros desafios aparecem diante deste modo de trabalho como, por exemplo, a sincronização dos relógios, que são prontamente solucionados pelo algoritmo proposto. Os resultados mostram que a solução proposta é eficiente e consegue economizar até 51.2% de energia com 99% na taxa de entrega de dados quando comparado com soluções similares.
... For example, the dynamic selection of CW values based on SL and RL methods can compensate for collisions and idle periods. In addition, self-learning has been used to reduce energy consumption [97], [98], support self-configuration [99], and select the MA scheme according to the environmental conditions and application requirements [100]. In summary, SL [101], [102], RL [103]- [106], deep reinforcement learning (DRL) [107]- [109], and federated learning (FL) [109], [110], models are applied to the IEEE 802. ...
With the rapidly increasing number of bandwidth-intensive terminals capable of intelligent computing and communication, such as smart devices equipped with shallow neural network models, the complexity of multiple access for these intelligent terminals is increasing due to the dynamic network environment and ubiquitous connectivity in 6G systems. Traditional multiple access (MA) design and optimization methods are gradually losing ground to artificial intelligence (AI) techniques that have proven their superiority in handling complexity. AI-empowered MA and its optimization strategies aimed at achieving high Quality-of-Service (QoS) are attracting more attention, especially in the area of latency-sensitive applications in 6G systems. In this work, we aim to: 1) present the development and comparative evaluation of AI-enabled MA; 2) provide a timely survey focusing on spectrum sensing, protocol design, and optimization for AI-empowered MA; and 3) explore the potential use cases of AI-empowered MA in the typical application scenarios within 6G systems. Specifically, we first present a unified framework of AI-empowered MA for 6G systems by incorporating various promising machine learning techniques in spectrum sensing, resource allocation, MA protocol design, and optimization. We then introduce AI-empowered MA spectrum sensing related to spectrum sharing and spectrum interference management. Next, we discuss the AI-empowered MA protocol designs and implementation methods by reviewing and comparing the state-of-the-art, and we further explore the optimization algorithms related to dynamic resource management, parameter adjustment, and access scheme switching. Finally, we discuss the current challenges, point out open issues, and outline potential future research directions in this field.
... To support the time-bounded performance of the network when developing time-bounded applications for WSNs, we also need to analyze the resource constraints and node-to-node transmission reliability. Numerous MAC and routing protocols with a focus on quality of service have recently been made available in wireless sensor networks [8][9][10]. Since most present protocols consider energy to be a valuable resource in sensors, they focus on increasing energy effectiveness. ...
Wireless Sensor Networks (WSNs) are expressively utilized in various real-time control and monitoring applications. WSNs have been expanded considering the necessities in industrial time-bounded applications to support the dependable, time-bound delivery of data. Recently, Machine Learning (ML) algorithms have been used to address various WSN-related issues. The use of machine learning techniques supports dynamically modifying MAC settings based on traffic patterns and network conditions. In WSNs to control the communication between a large number of tiny, low-power sensor nodes while preserving energy and reducing latency, effective MAC protocols are essential. This paper addresses the ML-based Adaptive MAC (ML-MAC) protocol to provide a priority-based transmission system. In this research, depending upon the predictions of the machine learning model, the MAC parameters are dynamically adjusted to find priority-based channel access and the optimal routing path to meet the deadline of critical data packets. From the result analysis, the average throughput and delay of the proposed ML-MAC algorithm is improved as compared to the existing I-MAC protocol.
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... Such an approach results in lower energy consumption as it the amount of idle listening is negligible. Dam et al. [16] proposed a time-out MAC (TMAC) adaptive protocol, while Lin et al. presented, in [17], a receiver initiated cycled receiver (RICER) as an example of a receiver-initiated scheme. It used a random delay between the reception of the beacon and the transmission of data. ...
Energy consumption is a major concern in wireless sensor networks (WSNs) as it affects the lifespan of sensor nodes. Battery-based WSNs have a short operating period, which makes them impractical for real-time applications, for instance in agriculture. Energy harvesting and suitable medium access control (MAC) protocols have been used to extend the lifetime of nodes. Receiver-initiated protocols have been proved to be the best solution for energy harvesting WSNs. However, they suffer from a key disadvantage, i.e. an increase in collision rate. These collisions need to be reduced using a multi-layer protocol structure. In such a context, a new solar-based hybrid MAC (SHMAC) protocol relying on receiver-initiation and characterized by a multi-layer structure is proposed. It is an adaptive protocol capable of adapting to changing weather conditions. The nodes with a high energy harvesting rate have a higher level of residual energy and are active for longer time periods compared with those with low energy harvesting characteristics. The proposed work has shown improvements in two major MAC layer parameters, i.e. collision rate and energy neutrality operation ratio (ENO).
... The MAC protocol was implemented as a standard unslotted, non-persistent carriersense multiple access (CSMA) protocol with acknowledged connectionless service. The implementation of Timeout-MAC [36] in Castalia served as the basis for implementing this protocol. Retransmissions are carried out after 5 ms in case no acknowledgment is received. ...
We consider the problem of meeting deadline constraints in wireless communication networks. Fulfilling deadlines depends heavily on the routing algorithm used. We study this dependence generically for a broad class of routing algorithms. For analyzing the impact of routing decisions on deadline fulfillment, we adopt a stochastic model from operations research to capture the source-to-destination delay distribution and the corresponding probability of successfully delivering data before a given deadline. Based on this model, we propose a decentralized algorithm that operates locally at each node and exchanges information solely with direct neighbors in order to determine the probabilities of achieving deadlines. A modified version of the algorithm also improves routing tables iteratively to progressively increase the deadline achievement probabilities. This modified algorithm is shown to deliver routing tables that maximize the deadline achievement probabilities for all nodes in a given network. We tested the approach by simulation and compared it with routing strategies based on established metrics, specifically the average delay, minimum hop count, and expected transmission count. Our evaluations encompass different channel quality and small-scale fading conditions, as well as various traffic load scenarios. Notably, our solution consistently outperforms the other approaches in all tested scenarios.
... However, the periodic listen-sleep schedule in S-MAC leads to increased latency. Several protocols, including T-MAC (Dam and Langendoen 2003), B-MAC (Polastre et al. 2003), Wise-MAC (El-Hoiydi and Decotignie 2004), X-MAC (Buettner et al. 2006), R-MAC (Du et al. 2007), and Z-MAC (Rhee et al. 2008), have been developed as alternatives to address latency, idle listening, and energy efficiency issues. One challenge in S-MAC is the premature death of border nodes that are situated at the boundary of two virtual clusters and must comply with the sleep/wake schedules of both. ...
In densely deployed sensor networks, numerous sensor nodes may record any event that takes place in the area. However, in such scenarios, multiple nodes may attempt to send the recorded data simultaneously, resulting in increased chances of collisions and energy wastage. In order to alleviate this problem, this paper proposes a new approach to minimize the number of copies generated by correlated sensor nodes. This is accomplished by grouping the sensor nodes into correlated regions and selecting only a subset of nodes to send their recorded data to the sink. The proposed approach incorporates a new medium access protocol that puts the selected sensor nodes in sleep mode when not required. Additionally, a spatial correlation model is developed to identify correlated regions, and a node selection algorithm is proposed to select the appropriate sensor nodes. Simulation results indicate that the proposed approach significantly reduces both energy consumption and reporting delay by exploiting correlation characteristics and optimal node selection.
The Industrial Internet of Things (IIoT) presents a complex landscape with numerous constraints, particularly due to their use to control critical applications in Industry 4.0. The requirements in such a context in terms of energy efficiency and quality of service (delay, reliability, determinism and robustness) are strict and of paramount importance. Consequently, there is a pressing need for sophisticated management mechanisms throughout their entire lifecycle to meet these needs. This thesis explores two technological fronts to address this challenge: Reinforcement Learning-based Time Slotted Channel Hopping (TSCH) scheduling and Network Digital Twin (NDT). TSCH scheduling in IIoT, is identified as a crucial area to optimize the performance of these networks. Several works proposed Reinforcement Learning-based scheduling techniques for TDMA (Time Division Multiple Access ) MAC protocols, and particularly for TSCH. However, using this approach in a constrained network like the IIoT carries the risk of elevated energy consumption. This is due to the continuous learning process and coordination among the nodes necessary to manage the non-stationarity issue in the network, which is viewed as a Multi-Agent System. This thesis introduces a novel Reinforcement Learning-based distributed scheduling algorithm, QL-TSCH-plus. This algorithm has been designed to be adaptive and efficient, with reduced energy consumption and delay targets inherent to IIoT environments. Parallel to the development of TSCH scheduling, this thesis adopts the concept of NDT as a viable solution for effective IIoT management. Digital twins have been increasingly used to optimize the performances of industrial systems. Capitalizing on this technology, a holistic NDT architecture for the IIoT is proposed, where the network is integrated with other industrial components. The architecture leverages Software Defined Networking to enable closed-loop network management across the entire network life-cycle (from design to service). This architecture enables quick validation of networking solutions in an industrial environment because of the continuous link between the NDT and the physical IIoT network. Moreover, we propose to model the IIoT in the NDT using Petri-nets, enabling data-driven Petri-nets. These serve as coarse-grained formal models enabling fast simulation time for what-if scenarios execution, and real-time fault detection that is crucial in mission-critical industrial applications.
Breast Breast cancer poses a significant global health concern, with approximately 2.2 million new cases and 700,000 deaths reported in 2020. Traditional diagnostic approaches which predominantly depend on expert judgement, have been associated with substantial variability in accuracy. To bridge this gap ML models are used to improve diagnostic out of which the present research investigates the potential of specific machine learning algorithms—D cancer remains one of the most common cancers among women globally, necessitating early detection to improve prognosis and survival rates. Recent advancements in machine learning (ML) have shown promise in enhancing the accuracy and efficiency of breast cancer and lesion detection. This review paper discusses the current methodologies, challenges, and future directions of ML applications in the detection and diagnosis of breast cancer. We analyze various ML algorithms used for analyzing mammograms, ultrasound, and MRI data, evaluating their effectiveness and applicability in clinical settings.
Cyber Physical Systems (CPS) is a complex system whose components are both physical and software being intertwined has emerged as a crucial domain and is capable of exhibiting multiple and distinct behavioral modalities handling real-world applications. Over the past two decades, they have evolved into a cornerstone for research and industrial applications, embodying a convergence of physical, biological, and engineered components governed by a computational core. It is a network of interacting elements with physical input and output devices. These systems heavily rely on advanced sensor nodes, communication technologies and control units. Addressing the challenge of deploying sensors in spatially-distributed processes, wireless sensor networks (WSNs) have taken center stage in CPS. WSNs offer a cost-effective solution for monitoring a diverse range of applications, from battlefield surveillance to environmental oversight. The integration of sensor devices within CPS is pivotal in ensuring precision in control and enhancing reliability. Several transdisciplinary approaches like merging theory of cybernetics, mechatronics, design and process science are involved in a CPS and the process is called as embedded system. Concurrently, technological progress has given rise to sophisticated cyber threats, necessitating ongoing vigilance from researchers to safeguard both physical and virtual systems. This necessitates security mitigation to take measures for reducing these harmful effects or hazards. Deep machine intelligence means machine intelligence based upon deep learning techniques and is most recent AI techniques. This chapter delves into the challenges faced from the security aspects of CPS and their solutions based upon deep machine intelligence, presenting experimental findings through an intrusion detection dataset.
Wireless body area networks (WBAN) provide remote services for patient monitoring which allows healthcare practitioners to diagnose, monitor, and prescribe them without their physical presence. To address the shortcomings of WBAN, software‐defined networking (SDN) is regarded as an effective approach in this prototype. However, integrating SDN into WBAN presents several challenges in terms of safe data exchange, architectural framework, and resource efficiency. Because energy expenses account for a considerable portion of network expenditures, energy efficiency has to turn out to be a crucial design criterion for modern networking methods. However, creating energy‐efficient systems is difficult because they must balance energy efficiency with network performance. In this article, the energy efficiency features are discussed that can widely be used in the software‐defined wireless body area network (SDWBAN). A comprehensive survey has been carried out for various modern energy efficiency models based on routing algorithms, optimization models, secure data delivery, and traffic management. A comparative assessment of all the models has also been carried out for various parameters. Furthermore, we explore important concerns and future work in SDWBAN energy efficiency.
The paper introduces OMNeT++, a C++-based discrete event simulation package primarily targeted at simulating computer networks and other distributed systems. OMNeT++ is fully programmable and modular, and it was designed from the ground up to support modeling very large networks built from reusable model components. Large emphasis was placed also on easy traceability and debuggability of simulation models: one can execute the simulation under a powerful graphical user interface, which makes the internals of a simulation model fully visible to the person running the simulation: it displays the network graphics, animates the message flow and lets the user peek into objects and variables within the model. These features make OMNeT++ a good candidate for both research and educational purposes. The OMNeT++ simulation engine can be easily embedded into larger applications. OMNeT++ is open-source, free for non-profit use, and it has a fairly large user community.
This paper proposes S-MAC, a medium-access control (MAC) protocol designed for wireless sensor networks. Wireless sensor networks use battery-operated computing and sensing devices. A network of these devices will collaborate for a common application such as environmental monitoring. We expect sensor networks to be deployed in an ad hoc fashion, with individual nodes remaining largely inactive for long periods of time, but then becoming suddenly active when something is detected. These characteristics of sensor networks and applications motivate a MAC that is different from traditional wireless MACs such as IEEE 802.11 in almost every way: energy conservation and self-configuration are primary goals, while per-node fairness and latency are less important. S-MAC uses three novel techniques to reduce energy consumption and support self-configuration. To reduce energy consumption in listening to an idle channel, nodes periodically sleep. Neighboring nodes form virtual clusters to auto-synchronize on sleep
In this paper we study the energy efficiency and channel efficiency of TDMA MAC protocol scheduling mechanisms. Most MAC protocols are based on phase grouping that basically has three phases in a frame: uplink, downlink and reservation. We propose a new mechanism in which we have multiple uplink and downlink phases. These phases are grouped per mobile in a frame. Although this has a negative effect on the capacity of the channel, it allows the mobile to turn the power off from the wireless interface for a longer period. We made this choice since in a mobile multimedia environment it is more important that connections have a certain QoS, than highest possible bandwidth. We present an analysis in which these two basic mechanisms are compared in respect to bandwidth efficiency and energy efficiency. We have developed and implemented a novel MAC protocol based on mobile grouping that provides Quality of Service (QoS) support for diverse traffic types.
ABSTRACT The paper introduces OMNeT++, a C++-based discrete event
Power-saving is a critical issue for almost all kinds of portable devices. In this paper, we consider the design of power-saving protocols for mobile ad hoc networks (MANETs) that allow mobile hosts to switch to a low-power sleep mode. The MANETs being considered in this paper are characterized by unpredictable mobility, multi-hop communication, and no clock synchronization mechanism. In particular, the last characteristic would complicate the problem since a host has to predict when another host will wake up to receive packets. We propose three power management protocols, namely dominating-awake-interval, periodically-fully-awake-interval, and quorum-based protocols, which are directly applicable to IEEE 802.11-based MANETs. As far as we know, the power management problem for multi-hop MANETs has not been seriously addressed in the literature. Existing standards, such as IEEE 802.11, HIPERLAN, and bluetooth, all assume that the network is fully connected or there is a clock synchronization mechanism. Extensive simulation results are presented to verify the effectiveness of the proposed protocols.
Low-power integration of sensing, communication, and computation requires a new approach to wireless design. Flexible interfaces and primitive accelerators enable aggressive system-level optimizations. Mica's flexible design serves as a building block for creating efficient application-specific protocols. Instead of defining narrow, standardized application interfaces, Mica provides a set of richly interconnected primitives (such as data serializers and timing extractors) to facilitate cross-layer optimizations. To explore novel systems approaches, researchers can develop customized protocols tailored to their application; Mica does not require use of predefined protocols.
In this paper we develop a new multiaccess protocol for ad hoc radio networks. The protocol is based on the original MACA protocol with the adition of a separate signalling channel. The unique feature of our protocol is that it conserves battery power at nodes by intelligently powering off nodes that are not actively transmitting or receiving packets. The manner in which nodes power themselves off does not influence the delay or throughput characteristics of our protocol. We illustrate the power conserving behavior of PAMAS via extensive simulations performed over ad hoc networks containing 10-20 nodes. Our results indicate that power savings of between 10% and 70% are attainable in most systems. Finally, we discuss how the idea of power awareness can be built into other multiaccess protocols as well.
n The existing Carrier Sense Multiple Access (CSMA) method widely used i mateur packet radio on shared simplex packet radio channels frequently suffers from
This paper proposes S-MAC, a medium-access control (MAC) protocol designed for wireless sensor networks. Wireless sensor networks use battery-operated computing and sensing devices; a network of these devices will collaborate for a common application such as environmental monitoring. We expect sensor networks to be deployed in an ad hoc fashion, with individual nodes remaining largely inactive for long periods of time, but then becoming suddenly active when something is detected. These characteristics of sensor networks and applications motivate a MAC that is different from traditional wireless MACs such as IEEE 802.11 in almost every way: energy conservation and selfconfiguration are a primary goals, while per-node fairness and latency are less important. S-MAC uses three novel techniques to reduce energy consumption and support selfconfiguration. To reduce energy consumption in listening to an idle channel, nodes periodically sleep. Neighboring nodes form virtual clusters to auto-synchronize on sleep schedules. Inspired by PAMAS [10], S-MAC also sets the radio to sleep during transmissions of other nodes. Unlike PAMAS, it only uses in-channel signaling. Finally, S-MAC applies message passing to reduce contention latency for sensor-network applications that require store-and-forward processing as data moves through the network. We evaluate our implementation of S-MAC over a sample sensor node, the Mote, developed at University of California, Berkeley (UCB). The experiment results show that, on a source node, an 802.11-like MAC consumes 2--6 times more energy than S-MAC for traffic load of 1--10s/message. Keywords--- Medium Access Control, Wireless Networks, Sensor Networks I.