Adaptive Duty Cycling MAC Protocols Using Closed-Loop Control for Wireless Sensor Networks

The fundamental design goal of wireless sensor MAC protocols is to minimize unnecessary power consumption of the sensor nodes, because of its stringent resource constraints and ultra-power limitation. In existing MAC protocols in wireless sensor networks (WSNs), duty cycling, in which each node periodically cycles between the active and sleep states, has been introduced to reduce unnecessary energy consumption. Existing MAC schemes, however, use a fixed duty cycling regardless of multi-hop communication and traffic fluctuations. On the other hand, there is a tradeoff between energy efficiency and delay caused by duty cycling mechanism in multi-hop communication and existing MAC approaches only tend to improve energy efficiency with sacrificing data delivery delay. In this paper, we propose two different MAC schemes (ADS-MAC and ELA-MAC) using closed-loop control in order to achieve both energy savings and minimal delay in wireless sensor networks. The two proposed MAC schemes, which are synchronous and asynchronous approaches, respectively, utilize an adaptive timer and a successive preload frame with closed-loop control for adaptive duty cycling. As a result, the analysis and the simulation results show that our schemes outperform existing schemes in terms of energy efficiency and delivery delay

Coordination and Self-Adaptive Communication Primitives for Low-Power Wireless Networks

The Internet of Things (IoT) is a recent trend where objects are augmented with computing and communication capabilities, often via low-power wireless radios. The Internet of Things is an enabler for a connected and more sustainable modern society: smart grids are deployed to improve energy production and consumption, wireless monitoring systems allow smart factories to detect faults early and reduce waste, while connected vehicles coordinate on the road to ensure our safety and save fuel. Many recent IoT applications have stringent requirements for their wireless communication substrate: devices must cooperate and coordinate, must perform efficiently under varying and sometimes extreme environments, while strict deadlines must be met. Current distributed coordination algorithms have high overheads and are unfit to meet the requirements of today\u27s wireless applications, while current wireless protocols are often best-effort and lack the guarantees provided by well-studied coordination solutions. Further, many communication primitives available today lack the ability to adapt to dynamic environments, and are often tuned during their design phase to reach a target performance, rather than be continuously updated at runtime to adapt to reality.In this thesis, we study the problem of efficient and low-latency consensus in the context of low-power wireless networks, where communication is unreliable and nodes can fail, and we investigate the design of a self-adaptive wireless stack, where the communication substrate is able to adapt to changes to its environment. We propose three new communication primitives: Wireless Paxos brings fault-tolerant consensus to low-power wireless networking, STARC is a middleware for safe vehicular coordination at intersections, while Dimmer builds on reinforcement learning to provide adaptivity to low-power wireless networks. We evaluate in-depth each primitive on testbed deployments and we provide an open-source implementation to enable their use and improvement by the community

MAC protocols for low-latency and energy-efficient WSN applications

Most of medium access control (MAC) protocols proposed for wireless sensor networks (WSN) are targeted only for single main objective, the energy efficiency. Other critical parameters such as low-latency, adaptivity to traffic conditions, scalability, system fairness, and bandwidth utilization are mostly overleaped or dealt as secondary objectives. The demand to address those issues increases with the growing interest in cheap, low-power, low- distance, and embedded WSNs. In this report, along with other vital parameters, we discuss suitability and limitations of different WSN MAC protocols for time critical and energy-efficient applications. As an example, we discuss the working of IEEE 802.15.4 in detail, explore its limitations, and derive efficient application-specific network parameter settings for time, energy, and bandwidth critical applications. Eventually, a new WSN MAC protocol Asynchronous Real-time Energy-efficient and Adaptive MAC (AREA-MAC) is proposed, which is intended to deal efficiently with time critical applications, and at the same time, to provide a better trade-off between other vital parameters, such as energy-efficiency, system fairness, throughput, scalability, and adaptivity to traffic conditions. On the other hand, two different optimization problems have been formulated using application-based traffic generating scenario to minimize network latency and maximize its lifetime

Beyond multimedia adaptation: Quality of experience-aware multi-sensorial media delivery

Multiple sensorial media (mulsemedia) combines multiple media elements which engage three or more of human senses, and as most other media content, requires support for delivery over the existing networks. This paper proposes an adaptive mulsemedia framework (ADAMS) for delivering scalable video and sensorial data to users. Unlike existing two-dimensional joint source-channel adaptation solutions for video streaming, the ADAMS framework includes three joint adaptation dimensions: video source, sensorial source, and network optimization. Using an MPEG-7 description scheme, ADAMS recommends the integration of multiple sensorial effects (i.e., haptic, olfaction, air motion, etc.) as metadata into multimedia streams. ADAMS design includes both coarse- and fine-grained adaptation modules on the server side: mulsemedia flow adaptation and packet priority scheduling. Feedback from subjective quality evaluation and network conditions is used to develop the two modules. Subjective evaluation investigated users' enjoyment levels when exposed to mulsemedia and multimedia sequences, respectively and to study users' preference levels of some sensorial effects in the context of mulsemedia sequences with video components at different quality levels. Results of the subjective study inform guidelines for an adaptive strategy that selects the optimal combination for video segments and sensorial data for a given bandwidth constraint and user requirement. User perceptual tests show how ADAMS outperforms existing multimedia delivery solutions in terms of both user perceived quality and user enjoyment during adaptive streaming of various mulsemedia content. In doing so, it highlights the case for tailored, adaptive mulsemedia delivery over traditional multimedia adaptive transport mechanisms

Prioritization-based adaptive emergency traffic medium access control protocol for wireless body area networks

Wireless Body Area Networks (WBANs) provide continuous monitoring of a patient by using heterogeneous Bio-Medical Sensor Nodes (BMSNs). WBANs pose unique constraints due to contention-based prioritized channel access, sporadic emergency traffic handling and emergency-based traffic adaptivity. In the existing medium access control protocols, the available contention-based prioritized channel access is incomplete due to the repetitions in backoff period ranges. The emergency traffic is considered based on traffic generation rate as well as sporadic emergency traffic that is not handled at multiple BMSNs during contention. In an emergency situation, non-emergency traffic is ignored, traffic is not adjusted dynamically with balanced throughput and energy consumption, and the energy of non-emergency traffic BMSNs is not preserved. In this research, prioritization-based adaptive emergency traffic Medium Access Control (MAC) protocol was designed to consider contention-based prioritized channel access for heterogenous BMSNs along with sporadic emergency traffic handling and dynamic adjustment of traffic in sporadic emergency situation. Firstly, a Traffic Class Prioritization based slotted-CSMA/CA (TCP-CSMA/CA) scheme was developed to provide contention-based prioritized channel access by removing repetitions in backoff period ranges. Secondly, an emergency Traffic Class Provisioning based slotted-CSMA/CA (ETCP-CSMA/CA) scheme was presented to deliver the sporadic emergency traffic instantaneously that occurs either at a single BMSN or multiple BMSNs, with minimum delay and packet loss without ignoring non-emergency traffic. Finally, an emergency-based Traffic Adaptive slotted-CSMA/CA (ETA-CSMA/CA) scheme provided dynamic adjustment of traffic to accommodate the variations in heterogeneous traffic rates along with energy preservation of non-emergency traffic BMSNs, creating a balance between throughput and energy in the sporadic emergency situation. Performance comparison was conducted by simulation using NS-2 and the results revealed that the proposed schemes were better than ATLAS, PLA-MAC, eMC-MAC and PG-MAC protocols. The least improved performances were in terms of packet delivery delay 10%, throughput 14%, packet delivery ratio 21%, packet loss ratio 28% and energy consumption 37%. In conclusion, the prioritization-based adaptive emergency traffic MAC protocol outperformed the existing protocols

Collaborative, Intelligent, and Adaptive Systems for the Low-Power Internet of Things

With the emergence of the Internet of Things (IoT), more and more devices are getting equipped with communication capabilities, often via wireless radios. Their deployments pave the way for new and mission-critical applications: cars will communicate with nearby vehicles to coordinate at intersections; industrial wireless closed-loop systems will improve operational safety in factories; while swarms of drones will coordinate to plan collision-free trajectories. To achieve these goals, IoT devices will need to communicate, coordinate, and collaborate over the wireless medium. However, these envisioned applications necessitate new characteristics that current solutions and protocols cannot fulfill: IoT devices require consistency guarantees from their communication and demand for adaptive behavior in complex and dynamic environments.In this thesis, we design, implement, and evaluate systems and mechanisms to enable safe coordination and adaptivity for the smallest IoT devices. To ensure consistent coordination, we bring fault-tolerant consensus to low-power wireless communication and introduce Wireless Paxos, a flavor of the Paxos algorithm specifically tailored to low-power IoT. We then present STARC, a wireless coordination mechanism for intersection management combining commit semantics with synchronous transmissions. To enable adaptivity in the wireless networking stack, we introduce Dimmer and eAFH. Dimmer combines Reinforcement Learning and Multi-Armed Bandits to adapt its communication parameters and counteract the adverse effects of wireless interference at runtime while optimizing energy consumption in normal conditions. eAFH provides dynamic channel management in Bluetooth Low Energy by excluding and dynamically re-including channels in scenarios with mobility. Finally, we demonstrate with BlueSeer that a device can classify its environment, i.e., recognize whether it is located in a home, office, street, or transport, solely from received Bluetooth Low Energy signals fed into an embedded machine learning model. BlueSeer therefore increases the intelligence of the smallest IoT devices, allowing them to adapt their behaviors to their current surroundings

Virtual Platform-Based Design Space Exploration of Power-Efficient Distributed Embedded Applications

Networked embedded systems are essential building blocks of a broad variety of distributed applications ranging from agriculture to industrial automation to healthcare and more. These often require specific energy optimizations to increase the battery lifetime or to operate using energy harvested from the environment. Since a dominant portion of power consumption is determined and managed by software, the software development process must have access to the sophisticated power management mechanisms provided by state-of-the-art hardware platforms to achieve the best tradeoff between system availability and reactivity. Furthermore, internode communications must be considered to properly assess the energy consumption. This article describes a design flow based on a SystemC virtual platform including both accurate power models of the hardware components and a fast abstract model of the wireless network. The platform allows both model-driven design of the application and the exploration of power and network management alternatives. These can be evaluated in different network scenarios, allowing one to exploit power optimization strategies without requiring expensive field trials. The effectiveness of the approach is demonstrated via experiments on a wireless body area network application

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial