Scheduling for IEEE802.15.4-TSCH and slow channel hopping MAC in low power industrial wireless networks

With growing needs to better understand our environments, the Internet-of-Things (IoT) is gaining importance among information and communication technologies. IoT will enable billions of intelligent devices and networks, such as wireless sensor networks, to be connected and integrated with computer networks. In order to support large scale networks, IETF has defined the routing protocol for low power and lossy networks (RPL) to facilitate the multihop connectivity. In this paper, we provide an in-depth review of current research activities. Specifically, the large scale simulation development and performance evaluation under various objective functions and routing metrics are pioneering works in RPL study. The results are expected to serve as a reference for evaluating the effectiveness of routing solutions in large scale IoT use cases.

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Performance Analysis of Routing Protocol for Low Power and Lossy Networks (RPL) in Large Scale Networks

To improve spectrum sensing performance, a cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm is proposed in this paper. In the process of signal feature extraction, a feature extraction method combining decomposition, recombination, and information geometry is proposed. First, to improve the spectrum sensing performance when the number of cooperative secondary users is small, the signals collected by the secondary users are split and reorganized, thereby logically increasing the number of cooperative secondary users. Then, in order to visually analyze the signal detection problem, the information geometry theory is used to map the split and recombine signals onto the manifold, thereby transforming the signal detection problem into a geometric problem. Further, use geometric tools to extract the corresponding statistical characteristics of the signal. Finally, according to the extracted features, the appropriate classifier is trained by the fuzzy c-means clustering algorithm and used for spectrum sensing, thus avoiding complex threshold derivation. In the simulation results and performance analysis section, the experimental results were further analyzed, and the results show that the proposed method can effectively improve the spectrum sensing performance.

/pdf/a-cooperative-spectrum-sensing-method-based-on-information-4uh7v5fmtx.pdf

A cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm

Cognitive radio in the context of internet of things using a novel future internet architecture called NovaGenesis

In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction, and maintenance of the packet transmission schedules are not defined. Moreover, to ensure optimal throughput, most of the existing scheduling methods are based on the assumption that instantaneous and accurate Channel State Information (CSI) is available. However, due to the inevitable errors in the channel estimation process, this assumption cannot be materialized in many practical scenarios. In this paper, we propose two alternative and realistic approaches. In our first approach, we assume that only the statistical knowledge of CSI is available a priori. Armed with this knowledge, the average packet rate on each link is computed and then, using the results, the throughput-optimal schedule for the assignment of (slot-frame) cells to links can be formulated as a max-weight bipartite matching problem, which can be solved efficiently using the well-known Hungarian algorithm. In the second approach, we assume that no CSI knowledge (even statistical) is available at the design stage. For this zero-knowledge setting, we introduce a machine learning-based algorithm by formally modeling the scheduling problem in terms of a combinatorial multi-armed bandit (CMAB) process. Our CMAB-based scheme is widely applicable to many real operational environments, thanks to its reduced reliance on design-time knowledge. Simulation results show that the average throughput obtained by the statistical CSI-based method is within the margin of 15% from the theoretical upper bound associated with perfect instantaneous CSI. The aforesaid margin is around 18% for our learning-theoretic solution.

pdf/ieee-802-15-4-e-tsch-based-scheduling-for-throughput-1u8y3a0ggk.pdf

IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach

According to recent studies, most of the radio spectrum is being underutilized. Cognitive radio has been proposed as a solution to address the problem of spectrum underutilization. It allows the secondary users to occupy spectrum allocated to primary users if primary users do not use the allocated spectrum at the current time and location. To detect unused spectrum cognitive radio must perform spectrum sensing. Spectrum sensing is an important function to be performed by every cognitive radio. Either single or multiple users can take part in spectrum sensing process. Later one is called Cooperative spectrum sensing. Cooperative spectrum sensing improves sensing performance by including a number of cognitive users rather than a single one. In cooperative spectrum sensing, each cognitive radio performs individual spectrum sensing by any of the available methods and individual sensing results are combined according to certain rules. Usually, current algorithms require signal knowledge, noise knowledge or the both to perform spectrum sensing. This paper describes the idea of blind spectrum sensing based on QR decomposition and linear prediction and extends it to cooperative sensing. It does not require either signal information or noise information to perform spectrum sensing. Simulation results show that the proposed method outperforms conventional energy based cooperative spectrum sensing under noise uncertainity. This algorithm is compared with blind source seperation method and significant improvement in performance is observed.

Performance analysis of blind spectrum sensing in cooperative environment

Radio spectrum is being underutilized. Cognitive Radio acts as a solution to the problem of spectrum under-utilization. Spectrum sensing is the most important feature of Cognitive Radio. Cooperative spectrum sensing improves sensing performance by including a number of cognitive users rather than a single one. In cooperative spectrum sensing, each cognitive radio performs individual spectrum sensing by any of the available methods and individual sensing results are combined according to certain rules. We propose a novel method where signal to noise ratio, credibility and position information of secondary users are used for spectrum sensing. They are much important factors that affect the sensing, but were not considered in previous works. The performance of this scheme is investigated by simulation results and is much better than the traditional ones. Significant improvement in detection probabilities are achieved reducing false alarm rates even in situations including malicious users and hidden primary user.

A novel approach in cooperative spectrum sensing for cognitive radio

Mobile Wireless Sensor Network(MWSN) is a spring up field of research in contrast to their well established predecessor-static WSN. The advantages of mobile wireless sensor network over static wireless sensor networks include better energy efficiency, improved coverage, enhanced target tracking, and superior channel capacity. They are much more versatile than WSN because they can be deployed in any scenario and cope with rapid topology changes. One of the main constraints for communication in MWSN is to maximize point-to-point connectivity between nodes. For this Distributed Scheduling is the optimal choice. Time Synchronized Channel Hopping (TSCH) is the suitable underlaying scheme for communication in MWSN. In this paper, Distributed scheduling algorithms for TSCH based MWSN is studied. Reservation based algorithm is used for scheduling and its performance is evaluated. Giving consideration to interference from co-located wireless systems utilizing the same spectral space in 2.4 GHz MWSN environment, scheduling technique is modified using Cognitive Scheme- Wavelet Packet analysis based Channel quality estimation. Simulation results shows that scheduling efficiency is improved about 25% using the proposed scheme.

Cognitive scheduling in TSCH based mobile WSN using wavelet packet analysis

Conventional wireless sensor networks (WSNs) consist of sensors with continuous transmission range, which depends on the relative positions of the transmitter and the receiver. However, sensors with different discrete transmission ranges are preferred for future generation low-power sensor networks because of certain functional advantages. The discrete transmission ranges introduce connectivity constraints in transmitting the sensor data. The proliferation of low-power WSNs has led to the explosion of the volume of the data to be processed. As the transmission of the data is the major cause of energy depletion of sensors that critically affect the network lifetime, energy-efficient aggregation of the sensor data is an important networking problem. In this work, we address the data aggregation problem in networks with sensors of discrete transmission ranges. We model the problem as a solvable integer linear program. However, this method applies only to networks of small sizes because of the hardness of the program. To solve the problem in networks of large sizes, we introduce a graphical framework that captures the characteristics of the networks with sensors of discrete transmission ranges and design a polynomial-time approximation technique to find a solution. Furthermore, we embed compression techniques based on compressed sensing (CS), which are established to yield high data compaction for temporally and spatially correlated distributed sensor data streams, and evaluate the performance of the proposed methods.

Data Aggregation in Low-Power Wireless Sensor Networks With Discrete Transmission Ranges: Sensor Signal Aggregation Over Graph

Cognitive Radio (CR) is viewed as a revolutionary potential radio technology capable of offering dynamic spectrum access. CR Networks (CRNs) with such spectrum aware devices is a hopeful solution to the spectrum scarcity issue in wireless communication sector. In this paper, an effective routing solution with a cross layer design is proposed for the multi-hop CRNs. Most of the existing work uses Ad-hoc On-demand Distance Vector (AODV) protocol as the routing protocol for CRNs. In this work, a novel routing algorithm based on Routing Protocol for Low power and lossy networks (RPL) is being proposed for the CRNs. Routing is accomplished through the formation of colored Destination Oriented Directed Acyclic Graphs (DODAGs) of which the link frequencies are represented using colors. Hop count and adjacent link interference are counted as the routing metrics. Dynamic spectrum allocation is also done along with routing with the help of RPL. The CRN scenario is vulnerable to link failures due to the appearance of Primary Users (PUs). So route repairing using the Trickle algorithm offered by RPL is also implemented in this work. To implement this algorithm, a traffic aware scheduling is used. A MATLAB simulation is done to analyze the effectiveness of the proposed algorithm.

Cross layer routing design based on RPL for multi-hop cognitive radio networks

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