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

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Energy-aware Scheduling of Surveillance in Wireless Multimedia Sensor Networks

Wireless sensor networks involve a large number of sensor nodes with limited energy supply, which impacts the behavior of their application. In wireless multimedia sensor networks, sensor nodes are equipped with audio and visual information collection modules. Multimedia contents are ubiquitously retrieved in surveillance applications. To solve the energy problems during target surveillance with wireless multimedia sensor networks, an energy-aware sensor scheduling method is proposed in this paper. Sensor nodes which acquire acoustic signals are deployed randomly in the sensing fields. Target localization is based on the signal energy feature provided by multiple sensor nodes, employing particle swarm optimization (PSO). During the target surveillance procedure, sensor nodes are adaptively grouped in a totally distributed manner. Specially, the target motion information is extracted by a forecasting algorithm, which is based on the hidden Markov model (HMM). The forecasting results are utilized to awaken sensor node in the vicinity of future target position. According to the two properties, signal energy feature and residual energy, the sensor nodes decide whether to participate in target detection separately with a fuzzy control approach. Meanwhile, the local routing scheme of data transmission towards the observer is discussed. Experimental results demonstrate the efficiency of energy-aware scheduling of surveillance in wireless multimedia sensor network, where significant energy saving is achieved by the sensor awakening approach and data transmission paths are calculated with low computational complexity

The Reputation of Machine Learning in Wireless Sensor Networks and Vehicular Ad Hoc Networks

It's difficult to deal with the dynamic nature of VANETs and WSNs in a way that makes sense. Machine learning (ML) is a preferred method for dealing with this kind of dynamicity. It is possible to define machine learning (ML) as a way of dealing with heterogeneous data in order to get the most out of a network without involving humans in the process or teaching it anything. Several techniques for WSN and VANETs based on ML are covered in this study, which provides a fast overview of the main ML ideas. Open difficulties and challenges in quickly changing networks, as well as diverse algorithms in relation to ML models and methodologies, are also covered in the following sections. We've provided a list of some of the most popular machine learning (ML) approaches for you to consider. As a starting point for further research, this article provides an overview of the various ML techniques and their difficulties. This paper's comparative examination of current state-of-the-art ML applications in WSN and VANETs is outstanding

Improving Human-Robot Cooperation and Safety In The Shared Automated Workplace

Modern industries take advantage of human-robot interaction to facilitate better manufacturing processes, particularly in applications where a human is working in a shared workplace with robots. In manufacturing settings where separation barriers, such as fences, are not used to protect human workers, approaches should be implemented for guaranteeing human safety. Despite existing methods, which define specifications and scenarios for human-robot cooperation in industry, new approaches are needed to provide a safer workplace while enhancing productivity. This thesis provides collision-free techniques for safe human-robot collaboration in an industrial setting. Human-robot interaction in the industry is studied to develop novel solutions and provide a secure and productive industrial environment. Providing a safe distance between a human worker and a manipulating robot, to prevent a collision, is an important subject of this work. This thesis presents a safe workplace by proposing an effective human-tracking method using a sensor network. The proposed technique utilizes a non-linear Kalman filter and Gaussian optimization to reduce the risk of collision between humans and robots. In this regard, selecting the most sensitive sensors to update the Kalman filter’s gain in a noisy environment is crucial. To this end, reliable sensor selection schemes are investigated, and a strategy based on multi-objective optimization is implemented.Finally, safe human-robot cooperation is investigated where humans work close to the robot or directly manipulate it in a shared task. In this case, the human’s hand is the most vulnerable limb and should be protected to achieve safe interaction. In this thesis, force myography (FMG) is used to detect the human hand activities to recognize hand gestures, detect the exerted force by a worker\u27s hand, and predict human intention. This information is then used to control the robot parameters, such as the gripper’s force. Furthermore, a human intention prediction scheme using FMG features and based on recurrent neural network (RNN) topology is proposed, to ensure safety during several industrial collaboration scenarios.The validity of the proposed approaches and the performance of the suggested control techniques are demonstrated through extensive simulation and practical experimentation. The results show that the proposed approaches will reduce the collision risk in human-robo

Energy Efficient Cooperation in Underlay RFID Cognitive Networks for a Water Smart Home

Shrinking water  resources all over the world and increasing  costs of water consumption  have prompted  water users  and distribution companies  to come up with water conserving strategies. We have proposed an energy-efficient  smart water monitoring application in [1], using low power RFIDs. In the home environment,  there exist many primary interferences within a room, such as cell-phones,  Bluetooth  devices, TV signals, cordless phones and WiFi devices.  In order to reduce the interference  from our proposed RFID network for these primary  devices, we have proposed a cooperating  underlay  RFID cognitive network for our smart application on water.  These underlay  RFIDs should strictly adhere to the interference thresholds to work in parallel with the primary wireless devices [2].  This work is an extension of our previous  ventures proposed in [2,3], and we enhanced the previous efforts by introducing  a new system model and RFIDs.  Our proposed scheme is mutually energy efficient and maximizes the signal-to-noise ratio (SNR) for the RFID link, while keeping the interference levels for the primary  network below a certain threshold. A closed form expression for the probability density function (pdf) of the SNR at the destination reader/writer and outage probability are derived. Analytical results are verified through simulations. It is also shown that in comparison to non-cognitive selective cooperation,  this scheme performs  better in the low SNR region for cognitive networks. Moreover, the hidden Markov model’s (HMM) multi-level variant hierarchical hidden Markov model (HHMM) approach is used for pattern recognition and event detection for the data received for this system [4]. Using this model, a feedback and decision algorithm is also developed.  This approach has been applied  to simulated water pressure data from RFID motes, which were embedded in metallic water pipes