Graph-theoretic channel modeling and topology control protocols for wireless sensor networks

This report addresses two different research problems: (i) It presents a wireless channel model that reduces the complexity associated with high order Markov chains; and (ii) presents energy efficient topology control protocols which provide reliability while maintaining the topology in an energy efficient manner. For the above problems, real wireless sensor network traces were collected and extensive simulations were performed for evaluating the proposed protocols. Accurate simulation and analysis of wireless networks are inherently dependent on accurate models which are able to provide real-time channel characterization. High-order Markov chains are typically used to model errors and losses over wireless channels. However, complexity (i.e., the number of states) of a high-order Markov model increases exponentially with the memory-length of the underlying channel. In this report, a novel graph-theoretic methodology that uses Hamiltonian circuits to reduce the complexity of a high-order Markov model to a desired state budget is presented. The implication of unused states in complexity reduction of higher order Markov model is also explained. The trace-driven performance evaluations for real wireless local area network (WLAN) and wireless sensor network (WSN) channels demonstrate that the proposed Hamiltonian Model, while providing orders of magnitude reduction in complexity, renders an accuracy that is comparable to the Markov model and better than the existing reduced state models. Furthermore, a methodology to preserve energy is presented to increase the network lifetime by reducing the node degree forming an active backbone while considering network connectivity. However, in energy stringent wireless sensor networks, it is of utmost importance to construct the reduced topology with the minimal control overhead. Moreover, most wireless links in practice are lossy links with connectivity probability which desires that a routing protocol provides routing flexibility and reliability at a minimum energy consumption cost. For this purpose, distributed and semi-distributed novel graph-theoretic topology construction protocols are presented that exploit cliques and polygons in a WSN to achieve energy efficiency and reliability. The proposed protocols also facilitate load rotation under topology maintenance, thereby extending the network lifetime. In addition to the above, the report also evaluates why the backbone construction using connected dominating set (CDS) in certain cases remains unable to provide connected sensing coverage in the area covered. For this purpose, a novel protocol that reduces the topology while considering sensing area coverage is presented

Topology control for harvesting enabled wireless sensor networks: a design approach

While there has been a lot of research on energy efficient topology control protocols destined for different applications, topology control has never been explored in the presence of harvesting enabled sensors. Largely, researchers in this domain have considered a fixed battery design. We argue that arrival of harvesting enabled sensors necessitates rethink of topology control. The objective of topology control in this context should not be to minimize the spent energy and maintain a reduced topology, but to maximize fault tolerance in the network and increase the sensing coverage region. In this work, we first describe a taxonomy of existing topology control schemes and analyze the impact of reduced topology over fault tolerance and sensing coverage. We then describe the necessity of new design parameters in the presence of harvest-able ambient energy. We also outline guiding principles for designing a harvesting enabled topology control scheme. To cater for whether such a scheme is feasible or not, an insight is also provided onto the solar energy availability from solar radiations for near perpetual operation—as an example of available ambient energy. Based on the insight gained from the solar radiations availability, we explain why new design parameters are required for performance measurement of harvesting enabled sensors. The mathematical and empirical findings reveal that the topology control strategies, which do not take into account harvesting opportunity, are unable to provide better results in terms of fault tolerance and sensing coverage

Variable rate adaptive modulation (VRAM) for introducing small-world model into WSNs

Data communication has a strong impact on the design of a Wireless Sensor Network (WSN), since the data transmission energy cost is typically higher than the data processing cost. In order to reduce the data transmission cost, small world phenomenon is introduced into WSNs. Networks that do not have the small world structure can be converted to achieve a small world property by the addition of few extra links. The problem is that most large scale WSNs are inherently unstructured and a node has no precise information of the overall model of the network and thus has to rely on the knowledge of its neighbor. For this reason, in most unstructured networks, information is propagated using gossiping. In this paper, we exploit this information propagation mechanism and use Neighbor Avoiding Walk (NAW), where the information is propagated to node that has not been visited previously and which is not the neighbor of a previously visited node. Using this, a novel approach is presented, in which nodes with highest betweenness centrality form a long distance relay path by using a lower order modulation scheme and therefore resulting in a relatively reduced data rate, but maintaining the same bit error rate. Our empirical and analytical evaluations demonstrate that this leads to a significant reduction in average path length and an increase in node degree

A lightweight blockchain based two factor authentication mechanism for LoRaWAN join procedure

Recently, there has been increasing interest in employing blockchain in different applications, other than crypto-currencies. Blockchains allow a peer to peer distributed network where different nodes communicate with each other, in a trustless manner. Long Range Wide Area Network (LoRaWAN) is an Internet of Things (IoT) technology, which enables long range communication. Although LoRaWAN networks are secure, the LoRaWAN join procedure is susceptible to replay and jamming attacks. Moreover, trust between network server and LoRa end device is the basic foundation of LoRaWAN network however, the centralized nature of network servers raise trust issues between network operators and customers. To solve this problem, we propose a lightweight two factor authentication mechanism for LoRaWAN join procedure, based on blockchain technology. The proposed blockchain based framework provides an extra layer of security for LoRaWAN join procedure and build trust among LoRaWAN network components. The proposed framework is validated using the Ethereum blockchain. The results demonstrate that the proposed framework provides efficient system performance in terms of throughput and latency. The proposed blockchain architecture is a cost effective solution, which can be utilized in the LoRaWAN network with few network servers and LoRa end device, having no strict requirement of throughput and latency

Energy management in harvesting enabled sensing nodes: prediction and control

Energy efficient transmission rate regulation of wireless sensing nodes, is a critical issue when operating in an energy harvesting (EH) enabled environment. In this work, we view the energy management problem as a queue control problem where the objective is to regulate transmission such that the energy level converges to a reference value. We employ a validated non-linear queuing model to derive two non-linear robust throughput controllers. A notable feature of the proposed scheme is its capability of predicting harvest-able energy. The predictions are generated using the proposed Accurate Solar Irradiance prediction Model (ASIM) whose effectiveness in generating accurate both long and short term predictions is demonstrated using real world data. The stability of the proposed controllers is established analytically and the effectiveness of the proposed strategies is demonstrated using simulations conducted on the Network Simulator (NS-3). The proposed policy is successful in guiding the energy level to the reference value, and outperforms the Throughput Optimal (TO) policy in terms of the throughput achieved

A1: An energy efficient topology control algorithm for connected area coverage in wireless sensor networks

Energy consumption in Wireless Sensor Networks(WSN’s) is of paramount importance, which is demonstrated by the large number of algorithms, techniques, and protocols that have been developed to save energy, and thereby extend the lifetime of the network. However, in the context of WSN’s routing and dissemination, Connected Dominating Set (CDS) principle has emerged as the most popular method for energy-efficient topology control (TC) in WSN’s. In a CDS-based topology control technique, a virtual backbone is formed which allows communication between any arbitrary pair of nodes in the network. In this paper, we present a CDS based topology control protocol – A1 – which forms an energy efficient virtual backbone. In our simulations, we compare the performance of A1 with three prominent CDS-based protocols namely Energy-efficient CDS (EECDS), CDS Rule K and A3. The results demonstrate that A1 performs consistently better in terms of message overhead and other selected metrics. Moreover, the A1 protocol not only achieves better connectivity under topology maintenance but also provides better sensing coverage when compared with the other protocols

Toward On-Device AI and Blockchain for 6G-Enabled Agricultural Supply Chain Management

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality of service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI, and blockchain for agricultural supply chain management with the purpose of ensuring traceability and transparency, tracking inventories, and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAVs, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G-enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements