Improving the energy efficiency for the WBSN bottleneck zone based on random linear network coding

The reduction of energy consumption and the successful delivery of data are important for the Wireless Body Sensor Network (WBSN). Many studies have been proposed to improve energy efficiency, but most of them have not focussed on the biosensor nodes in the WBSN bottleneck zone. Energy consumption is a critical issue in WBSNs, as the nodes that are placed next to the sink node consume more energy. All biomedical packets are aggregated through these nodes forming a bottleneck zone. This paper proposes a novel mathematical model for body area network (BAN) topology to explain the deployment and connection between biosensor nodes, simple relay nodes, network coding relay nodes and the sink node. Therefore, this paper is dedicated to researching both the energy saving and delivery of data if there is a failure in one of the links of the transmission, which relates to the proposed Random Linear Network Coding (RLNC) model in the WBSN. Using a novel mathematical model for a WBSN, it is apparent that energy consumption is reduced and data delivery achieved with the proposed mechanism. This paper details the stages of the research work

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

3R: a reliable multi-agent reinforcement learning based routing protocol for wireless medical sensor networks.

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraints, there is a need to develop a routing protocol to fulfill WMSN requirements in terms of delivery reliability, attack resiliency, computational overhead and energy efficiency. This paper proposes 3R, a reliable multi-agent reinforcement learning routing protocol for WMSN. 3R uses a novel resource-conservative Reinforcement Learning (RL) model to reduce the computational overhead, along with two updating methods to speed up the algorithm convergence. The reward function is re-defined as a punishment, combining the proposed trust management system to defend against well-known dropping attacks. Furthermore, an energy model is integrated with the reward function to enhance the network lifetime and balance energy consumption across the network. The proposed energy model uses only local information to avoid the resource burdens and the security concerns of exchanging energy information. Experimental results prove the lightweightness, attacks resiliency and energy efficiency of 3R, making it a potential routing candidate for WMSN

Energy saving and reliability for Wireless Body Sensor Networks (WBSN)

In healthcare and medical applications, the energy consumption of biosensor nodes affects the collection of biomedical data packets, which are sensed and measured from the human body and then transmitted toward the sink node. Nodes that are near to the sink node consume more energy as all biomedical packets are aggregated through these nodes when communicated to sink node. Each biosensor node in a wireless body sensor networks (WBSNs) such as ECG (Electrocardiogram), should provide accurate biomedical data due to the paramount importance of patient information. We propose a technique to minimise energy consumed by biosensor nodes in the bottleneck zone for WBSNs, which applies the Coordinated Duty Cycle Algorithm (CDCA) to all nodes in the bottleneck zone. Superframe order (SO) selection in CDCA is based on real traffic and the priority of the nodes in the WBSN. Furthermore, we use a special case of network coding, called Random Linear Network coding (RLNC), to encode the biomedical packets to improve reliability through calculating the probability of successful reception (PSR) at the sink node. It can be concluded that CDCA outperforms other algorithms in terms of energy saving as it achieves energy savings for most biosensor nodes in WBSNs. RLNC employs relay nodes to achieve the required level of reliability in WBSNs and to guarantee that the biomedical data is delivered correctly to the sink nod

A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks.

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraints, there is a need to develop a routing protocol to fulfill WMSN requirements in terms of delivery reliability, attack resiliency, computational overhead and energy efficiency. This doctoral research therefore aims to advance the state of the art in routing by proposing a lightweight, reliable routing protocol for WMSN. Ensuring a reliable path between the source and the destination requires making trustaware routing decisions to avoid untrustworthy paths. A lightweight and effective Trust Management System (TMS) has been developed to evaluate the trust relationship between the sensor nodes with a view to differentiating between trustworthy nodes and untrustworthy ones. Moreover, a resource-conservative Reinforcement Learning (RL) model has been proposed to reduce the computational overhead, along with two updating methods to speed up the algorithm convergence. The reward function is re-defined as a punishment, combining the proposed trust management system to defend against well-known dropping attacks. Furthermore, with a view to addressing the inborn overestimation problem in Q-learning-based routing protocols, we adopted double Q-learning to overcome the positive bias of using a single estimator. An energy model is integrated with the reward function to enhance the network lifetime and balance energy consumption across the network. The proposed energy model uses only local information to avoid the resource burdens and the security concerns of exchanging energy information. Finally, a realistic trust management testbed has been developed to overcome the limitations of using numerical analysis to evaluate proposed trust management schemes, particularly in the context of WMSN. The proposed testbed has been developed as an additional module to the NS-3 simulator to fulfill usability, generalisability, flexibility, scalability and high-performance requirements

Cross-layer MAC/routing protocol for reliable communication in Internet of Health Things

Internet of Health Things (IoHT) involves intelligent, low-powered, and miniaturized sensors nodes that measure physiological signals and report them to sink nodes over wireless links. IoHTs have a myriad of applications in e-health and personal health monitoring. Because of the data’s sensitivity measured by the nodes and power-constraints of the sensor nodes, reliability and energy-efficiency play a critical role in communication in IoHT. Reliability is degraded by the increase in packets’ loss due to inefficient MAC, routing protocols, environmental interference, and body shadowing. Simultaneously, inefficient node selection for routing may cause the depletion of critical nodes’ energy resources. Recent advancements in cross-layer protocol optimizations have proven their efficiency for packet-based Internet. In this article, we propose a MAC/Routing-based Cross-layer protocol for reliable communication while preserving the sensor nodes’ energy resource in IoHT. The proposed mechanism employs a timer-based strategy for relay node selection. The timer-based approach incorporates the metrics for residual energy and received signal strength indicator to preserve the vital underlying resources of critical sensors in IoHT. The proposed approach is also extended for multiple sensor networks, where sensor in vicinity are coordinating and cooperating for data forwarding. The performance of the proposed technique is evaluated for metrics like Packet Loss Probability, End-To-End delay, and energy used per data packet. Extensive simulation results show that the proposed technique improves the reliability and energy-efficiency compared to the Simple Opportunistic Routing protocol

Artificial iIntelligence for Big Data: issues and challenges

Artificial intelligence (AI) concerns the study and development of intelligent ma-chines and software. The associated ICT research is highly technical and specialized, and its focal problems include the developments of software that can reason, gather knowledge, plan intelligently, learn, communicate, perceive and manipulate objects. AI also allows users of big data to automate and enhance complex descriptive and predictive analytical tasks that, when performed by humans, would be extremely la-bour intensive and time consuming. Thus, unleashing AI on big data can have a sig-nificant impact on the role data plays in deciding how we work, how we travel and how we conduct business. This paper explores how Artificial Intelligence, in conjunc-tion with Big Data technologies, can help organizations to bring about operational and business transformation.Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for network design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches

Wireless Communications and Mobile Computing using Machine learning

This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not re-place, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is present-ed. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely-used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for net-work design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches